Sort results of hashes in _hash_dir to always get the same result, remove dependency on os.walk

Currently translated at 94.6% (1431 of 1512 strings)

translationBot(ui): update translation (Russian)

Currently translated at 94.6% (1431 of 1512 strings)

Co-authored-by: Васянатор <ilabulanov339@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI

Makefile

@@ -10,10 +10,11 @@ help:
 	@echo "ruff-unsafe       Run ruff, fixing all fixable errors and formatting"
 	@echo "mypy              Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors"
 	@echo "mypy-all          Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
-	@echo "test"             Run the unit tests.
-	@echo "frontend-install" Install the pnpm modules needed for the front end
+	@echo "test              Run the unit tests."
+	@echo "frontend-install  Install the pnpm modules needed for the front end"
 	@echo "frontend-build    Build the frontend in order to run on localhost:9090"
 	@echo "frontend-dev      Run the frontend in developer mode on localhost:5173"
+	@echo "frontend-typegen  Generate types for the frontend from the OpenAPI schema"
 	@echo "installer-zip     Build the installer .zip file for the current version"
 	@echo "tag-release       Tag the GitHub repository with the current version (use at release time only!)"
 
@@ -53,6 +54,9 @@ frontend-build:
 frontend-dev:
 	cd invokeai/frontend/web && pnpm dev
 
+frontend-typegen:
+	cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen
+
 # Installer zip file
 installer-zip:
 	cd installer && ./create_installer.sh

docs/contributing/MODEL_MANAGER.md

@@ -32,7 +32,6 @@ model. These are the:
   Responsible for loading a model from disk
   into RAM and VRAM and getting it ready for inference.
 
-  
 ## Location of the Code
 
 The four main services can be found in
@@ -63,23 +62,21 @@ provides the following fields:
 |----------------|-----------------|------------------|
 | `key`            | str           | Unique identifier for the model |
 | `name`           | str           | Name of the model (not unique) |
-| `model_type`     | ModelType     | The type of the model | 
-| `model_format`   | ModelFormat   | The format of the model (e.g. "diffusers"); also used as a Union discriminator | 
-| `base_model`     | BaseModelType | The base model that the model is compatible with | 
+| `model_type`     | ModelType     | The type of the model |
+| `model_format`   | ModelFormat   | The format of the model (e.g. "diffusers"); also used as a Union discriminator |
+| `base_model`     | BaseModelType | The base model that the model is compatible with |
 | `path`           | str           | Location of model on disk |
-| `original_hash`  | str           | Hash of the model when it was first installed |
-| `current_hash`   | str           | Most recent hash of the model's contents |
+| `hash`           | str           | Hash of the model |
 | `description`    | str           | Human-readable description of the model (optional) |
 | `source`         | str           | Model's source URL or repo id (optional) |
 
 The `key` is a unique 32-character random ID which was generated at
-install time. The `original_hash` field stores a hash of the model's
+install time. The `hash` field stores a hash of the model's
 contents at install time obtained by sampling several parts of the
 model's files using the `imohash` library. Over the course of the
 model's lifetime it may be transformed in various ways, such as
 changing its precision or converting it from a .safetensors to a
-diffusers model. When this happens, `original_hash` is unchanged, but
-`current_hash` is updated to indicate the current contents.
+diffusers model.
 
 `ModelType`, `ModelFormat` and `BaseModelType` are string enums that
 are defined in `invokeai.backend.model_manager.config`. They are also
@@ -94,7 +91,6 @@ The `path` field can be absolute or relative. If relative, it is taken
 to be relative to the `models_dir` setting in the user's
 `invokeai.yaml` file.
 
-
 ### CheckpointConfig
 
 This adds support for checkpoint configurations, and adds the
@@ -174,7 +170,7 @@ store = context.services.model_manager.store
 or from elsewhere in the code by accessing
 `ApiDependencies.invoker.services.model_manager.store`.
 
-### Creating a `ModelRecordService` 
+### Creating a `ModelRecordService`
 
 To create a new `ModelRecordService` database or open an existing one,
 you can directly create either a `ModelRecordServiceSQL` or a
@@ -217,27 +213,27 @@ for use in the InvokeAI web server. Its signature is:
 ```
 def open(
        cls, 
-	   config: InvokeAIAppConfig, 
-	   conn: Optional[sqlite3.Connection] = None, 
-	   lock: Optional[threading.Lock] = None
+    config: InvokeAIAppConfig, 
+    conn: Optional[sqlite3.Connection] = None, 
+    lock: Optional[threading.Lock] = None
     ) -> Union[ModelRecordServiceSQL, ModelRecordServiceFile]:
 ```
 
 The way it works is as follows:
 
 1. Retrieve the value of the `model_config_db` option from the user's
-	`invokeai.yaml` config file.
+ `invokeai.yaml` config file.
 2. If `model_config_db` is `auto` (the default), then:
-   - Use the values of `conn` and `lock` to return a `ModelRecordServiceSQL` object
-	 opened on the passed connection and lock.
-   - Open up a new connection to `databases/invokeai.db` if `conn`
+   * Use the values of `conn` and `lock` to return a `ModelRecordServiceSQL` object
+  opened on the passed connection and lock.
+   * Open up a new connection to `databases/invokeai.db` if `conn`
      and/or `lock` are missing (see note below).
 3. If `model_config_db` is a Path, then use `from_db_file`
    to return the appropriate type of ModelRecordService.
 4. If `model_config_db` is None, then retrieve the legacy
    `conf_path` option from `invokeai.yaml` and use the Path
    indicated there. This will default to `configs/models.yaml`.
-   
+
 So a typical startup pattern would be:
 
 ```
@@ -255,7 +251,7 @@ store = ModelRecordServiceBase.open(config, db_conn, lock)
 
 Configurations can be retrieved in several ways.
 
-#### get_model(key) -> AnyModelConfig:
+#### get_model(key) -> AnyModelConfig
 
 The basic functionality is to call the record store object's
 `get_model()` method with the desired model's unique key. It returns
@@ -272,28 +268,28 @@ print(model_conf.path)
 If the key is unrecognized, this call raises an
 `UnknownModelException`.
 
-#### exists(key) -> AnyModelConfig:
+#### exists(key) -> AnyModelConfig
 
 Returns True if a model with the given key exists in the databsae.
 
-#### search_by_path(path) -> AnyModelConfig:
+#### search_by_path(path) -> AnyModelConfig
 
 Returns the configuration of the model whose path is `path`. The path
 is matched using a simple string comparison and won't correctly match
 models referred to by different paths (e.g. using symbolic links).
 
-#### search_by_name(name, base, type) -> List[AnyModelConfig]:
+#### search_by_name(name, base, type) -> List[AnyModelConfig]
 
 This method searches for models that match some combination of `name`,
 `BaseType` and `ModelType`. Calling without any arguments will return
 all the models in the database.
 
-#### all_models() -> List[AnyModelConfig]:
+#### all_models() -> List[AnyModelConfig]
 
 Return all the model configs in the database. Exactly equivalent to
 calling `search_by_name()` with no arguments.
 
-#### search_by_tag(tags) -> List[AnyModelConfig]:
+#### search_by_tag(tags) -> List[AnyModelConfig]
 
 `tags` is a list of strings. This method returns a list of model
 configs that contain all of the given tags. Examples:
@@ -312,11 +308,11 @@ commercializable_models = [x for x in store.all_models() \
                            if x.license.contains('allowCommercialUse=Sell')]
 ```
 
-#### version() -> str:
+#### version() -> str
 
 Returns the version of the database, currently at `3.2`
 
-#### model_info_by_name(name, base_model, model_type) -> ModelConfigBase:
+#### model_info_by_name(name, base_model, model_type) -> ModelConfigBase
 
 This method exists to ease the transition from the previous version of
 the model manager, in which `get_model()` took the three arguments
@@ -337,7 +333,7 @@ model and pass its key to `get_model()`.
 Several methods allow you to create and update stored model config
 records.
 
-#### add_model(key, config) -> AnyModelConfig:
+#### add_model(key, config) -> AnyModelConfig
 
 Given a key and a configuration, this will add the model's
 configuration record to the database. `config` can either be a subclass of
@@ -352,7 +348,7 @@ model with the same key is already in the database, or an
 `InvalidModelConfigException` if a dict was passed and Pydantic
 experienced a parse or validation error.
 
-### update_model(key, config) -> AnyModelConfig:
+### update_model(key, config) -> AnyModelConfig
 
 Given a key and a configuration, this will update the model
 configuration record in the database. `config` can be either a
@@ -370,31 +366,31 @@ The `ModelInstallService` class implements the
 shop for all your model install needs. It provides the following
 functionality:
 
-- Registering a model config record for a model already located on the
+* Registering a model config record for a model already located on the
   local filesystem, without moving it or changing its path.
   
-- Installing a model alreadiy located on the local filesystem, by
+* Installing a model alreadiy located on the local filesystem, by
   moving it into the InvokeAI root directory under the
   `models` folder (or wherever config parameter `models_dir`
   specifies).
-	
-- Probing of models to determine their type, base type and other key
+
+* Probing of models to determine their type, base type and other key
   information.
   
-- Interface with the InvokeAI event bus to provide status updates on
+* Interface with the InvokeAI event bus to provide status updates on
   the download, installation and registration process.
   
-- Downloading a model from an arbitrary URL and installing it in
+* Downloading a model from an arbitrary URL and installing it in
   `models_dir`.
   
-- Special handling for Civitai model URLs which allow the user to
+* Special handling for Civitai model URLs which allow the user to
   paste in a model page's URL or download link
   
-- Special handling for HuggingFace repo_ids to recursively download
+* Special handling for HuggingFace repo_ids to recursively download
   the contents of the repository, paying attention to alternative
   variants such as fp16.
   
-- Saving tags and other metadata about the model into the invokeai database
+* Saving tags and other metadata about the model into the invokeai database
   when fetching from a repo that provides that type of information,
   (currently only Civitai and HuggingFace).
   
@@ -427,8 +423,8 @@ queue.start()
 
 installer = ModelInstallService(app_config=config, 
                                 record_store=record_store,
-						        download_queue=queue
-							    )
+              download_queue=queue
+           )
 installer.start()
 ```
 
@@ -443,7 +439,6 @@ required parameters:
 | `metadata_store`   | Optional[ModelMetadataStore]  | Metadata storage object |
 |`session`           | Optional[requests.Session]    | Swap in a different Session object (usually for debugging) |
 
-
 Once initialized, the installer will provide the following methods:
 
 #### install_job = installer.heuristic_import(source, [config], [access_token])
@@ -457,15 +452,15 @@ 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
+   * `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. 
+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`
@@ -491,9 +486,9 @@ following illustrates basic usage:
 
 ```
 from invokeai.app.services.model_install import (
-	LocalModelSource,
-	HFModelSource,
-	URLModelSource,
+ LocalModelSource,
+ HFModelSource,
+ URLModelSource,
 )
 
 source1 = LocalModelSource(path='/opt/models/sushi.safetensors')   # a local safetensors file
@@ -513,13 +508,13 @@ for source in [source1, source2, source3, source4, source5, source6, source7]:
 source2job = installer.wait_for_installs(timeout=120)
 for source in sources:
     job = source2job[source]
-	if job.complete:
-		model_config = job.config_out
-		model_key = model_config.key
-		print(f"{source} installed as {model_key}")
-	elif job.errored:
-	    print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
-	
+ if job.complete:
+  model_config = job.config_out
+  model_key = model_config.key
+  print(f"{source} installed as {model_key}")
+ elif job.errored:
+     print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
+ 
 ```
 
 As shown here, the `import_model()` method accepts a variety of
@@ -528,7 +523,7 @@ HuggingFace repo_ids with and without a subfolder designation,
 Civitai model URLs and arbitrary URLs that point to checkpoint files
 (but not to folders).
 
-Each call to `import_model()` return a `ModelInstallJob` job, 
+Each call to `import_model()` return a `ModelInstallJob` job,
 an object which tracks the progress of the install.
 
 If a remote model is requested, the model's files are downloaded in
@@ -555,7 +550,7 @@ The full list of arguments to `import_model()` is as follows:
 | `config`         | Dict[str, Any]               | None        | Override all or a portion of model's probed attributes |
 
 The next few sections describe the various types of ModelSource that
-can be passed to `import_model()`. 
+can be passed to `import_model()`.
 
 `config` can be used to override all or a portion of the configuration
 attributes returned by the model prober. See the section below for
@@ -566,7 +561,6 @@ details.
 This is used for a model that is located on a locally-accessible Posix
 filesystem, such as a local disk or networked fileshare.
 
-
 | **Argument**     | **Type**                     | **Default** | **Description**                           |
 |------------------|------------------------------|-------------|-------------------------------------------|
 | `path`           | str | Path                   | None        | Path to the model file or directory |
@@ -625,7 +619,6 @@ HuggingFace has the most complicated `ModelSource` structure:
 | `subfolder`      | Path                         | None        | Look for the model in a subfolder of the repo. |
 | `access_token`   | str                          | None        | An access token needed to gain access to a subscriber's-only model. |
 
-
 The `repo_id` is the repository ID, such as `stabilityai/sdxl-turbo`.
 
 The `variant` is one of the various diffusers formats that HuggingFace
@@ -661,7 +654,6 @@ in. To download these files, you must provide an
 `HfFolder.get_token()` will be called to fill it in with the cached
 one.
 
-
 #### Monitoring the install job process
 
 When you create an install job with `import_model()`, it launches the
@@ -675,14 +667,13 @@ The `ModelInstallJob` class has the following structure:
 | `id`           | `int`           | Integer ID for this job |
 | `status`       | `InstallStatus`  | An enum of [`waiting`, `downloading`, `running`, `completed`, `error` and `cancelled`]|
 | `config_in`    | `dict`          | Overriding configuration values provided by the caller |
-| `config_out`   | `AnyModelConfig`| After successful completion, contains the configuration record written to the database | 
-| `inplace`      | `boolean`       | True if the caller asked to install the model in place using its local path | 
-| `source`       | `ModelSource`   | The local path, remote URL or repo_id of the model to be installed | 
+| `config_out`   | `AnyModelConfig`| After successful completion, contains the configuration record written to the database |
+| `inplace`      | `boolean`       | True if the caller asked to install the model in place using its local path |
+| `source`       | `ModelSource`   | The local path, remote URL or repo_id of the model to be installed |
 | `local_path`   | `Path`          | If a remote model, holds the path of the model after it is downloaded; if a local model, same as `source` |
 | `error_type`   | `str`           | Name of the exception that led to an error status |
 | `error`        | `str`           | Traceback of the error |
 
-
 If the `event_bus` argument was provided, events will also be
 broadcast to the InvokeAI event bus. The events will appear on the bus
 as an event of type `EventServiceBase.model_event`, a timestamp and
@@ -702,14 +693,13 @@ following keys:
 | `total_bytes`  | int       | Total size of all the files that make up the model |
 | `parts`        | List[Dict]| Information on the progress of the individual files that make up the model |
 
-
 The parts is a list of dictionaries that give information on each of
 the components pieces of the download. The dictionary's keys are
 `source`, `local_path`, `bytes` and `total_bytes`, and correspond to
 the like-named keys in the main event.
 
 Note that downloading events will not be issued for local models, and
-that downloading events occur *before* the running event.
+that downloading events occur _before_ the running event.
 
 ##### `model_install_running`
 
@@ -752,14 +742,13 @@ properties: `waiting`, `downloading`, `running`, `complete`, `errored`
 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
 
 The install service uses the `invokeai.backend.model_manager.probe`
 module during import to determine the model's type, base type, and
 other configuration parameters. Among other things, it assigns a
 default name and description for the model based on probed
-fields. 
+fields.
 
 When downloading remote models is implemented, additional
 configuration information, such as list of trigger terms, will be
@@ -774,11 +763,11 @@ attributes. Here is an example of setting the
 ```
 install_job = installer.import_model(
                source=HFModelSource(repo_id='stabilityai/stable-diffusion-2-1',variant='fp32'),
-			   config=dict(
-			         prediction_type=SchedulerPredictionType('v_prediction')
-					 name='stable diffusion 2 base model',
-	           )
-	      )
+      config=dict(
+            prediction_type=SchedulerPredictionType('v_prediction')
+      name='stable diffusion 2 base model',
+            )
+       )
 ```
 
 ### Other installer methods
@@ -862,7 +851,6 @@ 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,
@@ -953,7 +941,7 @@ following fields:
 
 When you create a job, you can assign it a `priority`. If multiple
 jobs are queued, the job with the lowest priority runs first. (Don't
-blame me! The Unix developers came up with this convention.) 
+blame me! The Unix developers came up with this convention.)
 
 Every job has a `source` and a `destination`. `source` is a string in
 the base class, but subclassses redefine it more specifically.
@@ -974,7 +962,7 @@ is in its lifecycle. Values are defined in the string enum
 `DownloadJobStatus`, a symbol available from
 `invokeai.app.services.download_manager`. Possible values are:
 
-| **Value**    |   **String Value**  | ** Description ** |
+| **Value**    |   **String Value**  | **Description** |
 |--------------|---------------------|-------------------|
 | `IDLE`         | idle              | Job created, but not submitted to the queue |
 | `ENQUEUED`     | enqueued          | Job is patiently waiting on the queue       |
@@ -991,7 +979,7 @@ debugging and performance testing.
 
 In case of an error, the Exception that caused the error will be
 placed in the `error` field, and the job's status will be set to
-`DownloadJobStatus.ERROR`. 
+`DownloadJobStatus.ERROR`.
 
 After an error occurs, any partially downloaded files will be deleted
 from disk, unless `preserve_partial_downloads` was set to True at job
@@ -1040,11 +1028,11 @@ While a job is being downloaded, the queue will emit events at
 periodic intervals. A typical series of events during a successful
 download session will look like this:
 
-- enqueued
-- running
-- running
-- running
-- completed
+* enqueued
+* running
+* running
+* running
+* completed
 
 There will be a single enqueued event, followed by one or more running
 events, and finally one `completed`, `error` or `cancelled`
@@ -1053,12 +1041,12 @@ events.
 It is possible for a caller to pause download temporarily, in which
 case the events may look something like this:
 
-- enqueued
-- running
-- running
-- paused
-- running
-- completed
+* enqueued
+* running
+* running
+* paused
+* running
+* completed
 
 The download queue logs when downloads start and end (unless `quiet`
 is set to True at initialization time) but doesn't log any progress
@@ -1120,11 +1108,11 @@ A typical initialization sequence will look like:
 from invokeai.app.services.download_manager import DownloadQueueService
 
 def log_download_event(job: DownloadJobBase):
-	logger.info(f'job={job.id}: status={job.status}')
+ logger.info(f'job={job.id}: status={job.status}')
 
 queue = DownloadQueueService(
-					  event_handlers=[log_download_event]
-						  )
+       event_handlers=[log_download_event]
+        )
 ```
 
 Event handlers can be provided to the queue at initialization time as
@@ -1155,9 +1143,9 @@ To use the former method, follow this example:
 ```
 job = DownloadJobRemoteSource(
          source='http://www.civitai.com/models/13456',
-		 destination='/tmp/models/',
-		 event_handlers=[my_handler1, my_handler2], # if desired
-		 )
+   destination='/tmp/models/',
+   event_handlers=[my_handler1, my_handler2], # if desired
+   )
 queue.submit_download_job(job, start=True)
 ```
 
@@ -1172,13 +1160,13 @@ To have the queue create the job for you, follow this example instead:
 ```
 job = queue.create_download_job(
          source='http://www.civitai.com/models/13456',
-		 destdir='/tmp/models/',
-		 filename='my_model.safetensors',
-		 event_handlers=[my_handler1, my_handler2], # if desired
-		 start=True,
-	)
+   destdir='/tmp/models/',
+   filename='my_model.safetensors',
+   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,
 and is equivalent to manually specifying a destination of
@@ -1187,7 +1175,6 @@ and is equivalent to manually specifying a destination of
 Here is the full list of arguments that can be provided to
 `create_download_job()`:
 
-
 | **Argument**     | **Type**                     | **Default** | **Description**                           |
 |------------------|------------------------------|-------------|-------------------------------------------|
 | `source`         | Union[str, Path, AnyHttpUrl] |             | Download remote or local source           |
@@ -1200,7 +1187,7 @@ Here is the full list of arguments that can be provided to
 
 Internally, `create_download_job()` has a little bit of internal logic
 that looks at the type of the source and selects the right subclass of
-`DownloadJobBase` to create and enqueue. 
+`DownloadJobBase` to create and enqueue.
 
 **TODO**: move this logic into its own method for overriding in
 subclasses.
@@ -1275,7 +1262,7 @@ for getting the model to run. For example "author" is metadata, while
 "type", "base" and "format" are not. The latter fields are part of the
 model's config, as defined in `invokeai.backend.model_manager.config`.
 
-### Example Usage:
+### Example Usage
 
 ```
 from invokeai.backend.model_manager.metadata import (
@@ -1328,7 +1315,6 @@ This is the common base class for metadata:
 | `author`      | str           | Model's author |
 | `tags`        | Set[str]      | Model tags |
 
-
 Note that the model config record also has a `name` field. It is
 intended that the config record version be locally customizable, while
 the metadata version is read-only. However, enforcing this is expected
@@ -1348,7 +1334,6 @@ This descends from `ModelMetadataBase` and adds the following fields:
 | `last_modified`| datetime         | Date of last commit of this model to the repo |
 | `files`        | List[Path]       | List of the files in the model repo |
 
-
 #### `CivitaiMetadata`
 
 This descends from `ModelMetadataBase` and adds the following fields:
@@ -1415,7 +1400,6 @@ testing suite to avoid hitting the internet.
 The HuggingFace and Civitai fetcher subclasses add additional
 repo-specific fetching methods:
 
-
 #### HuggingFaceMetadataFetch
 
 This overrides its base class `from_json()` method to return a
@@ -1434,13 +1418,12 @@ retrieves its metadata. Functionally equivalent to `from_id()`, the
 only difference is that it returna a `CivitaiMetadata` object rather
 than an `AnyModelRepoMetadata`.
 
-
 ### Metadata Storage
 
 The `ModelMetadataStore` provides a simple facility to store model
 metadata in the `invokeai.db` database. The data is stored as a JSON
 blob, with a few common fields (`name`, `author`, `tags`) broken out
-to be searchable. 
+to be searchable.
 
 When a metadata object is saved to the database, it is identified
 using the model key, _and this key must correspond to an existing
@@ -1535,16 +1518,16 @@ from invokeai.app.services.model_load import ModelLoadService, ModelLoaderRegist
 
 config = InvokeAIAppConfig.get_config()
 ram_cache = ModelCache(
-	max_cache_size=config.ram_cache_size, max_vram_cache_size=config.vram_cache_size, logger=logger
+ 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
+ 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
+ app_config=config,
+ ram_cache=ram_cache,
+ convert_cache=convert_cache,
+ registry=ModelLoaderRegistry
 )
 ```
 
@@ -1567,7 +1550,6 @@ 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:
 
-
 | **Attribute Name** | **Type**        |  **Description** |
 |----------------|-----------------|------------------|
 | `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
@@ -1581,7 +1563,6 @@ return `AnyModel`, a Union `ModelMixin`, `torch.nn.Module`,
 models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
 models. The others are obvious.
 
-
 `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
@@ -1590,14 +1571,14 @@ the model. Use it like this:
 ```
 model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
 with model_info as vae:
-	image = vae.decode(latents)[0]
+ image = vae.decode(latents)[0]
 ```
 
 `get_model_by_key()` may raise any of the following exceptions:
 
-- `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
+* `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
   
 ### Emitting model loading events
 
@@ -1609,15 +1590,15 @@ following payload:
 
 ```
 payload=dict(
-	queue_id=queue_id,
-	queue_item_id=queue_item_id,
-	queue_batch_id=queue_batch_id,
-	graph_execution_state_id=graph_execution_state_id,
-	model_key=model_key,
-	submodel_type=submodel,
-	hash=model_info.hash,
-	location=str(model_info.location),
-	precision=str(model_info.precision),
+ queue_id=queue_id,
+ queue_item_id=queue_item_id,
+ queue_batch_id=queue_batch_id,
+ graph_execution_state_id=graph_execution_state_id,
+ model_key=model_key,
+ submodel_type=submodel,
+ hash=model_info.hash,
+ location=str(model_info.location),
+ precision=str(model_info.precision),
 )
 ```
 
@@ -1724,6 +1705,7 @@ 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
 ```

invokeai/app/api/dependencies.py

@@ -25,8 +25,8 @@ from ..services.invocation_cache.invocation_cache_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.model_images.model_images_default import ModelImageFileStorageDisk
 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
@@ -72,6 +72,8 @@ class ApiDependencies:
 
         image_files = DiskImageFileStorage(f"{output_folder}/images")
 
+        model_images_folder = config.models_path
+
         db = init_db(config=config, logger=logger, image_files=image_files)
 
         configuration = config
@@ -93,10 +95,10 @@ class ApiDependencies:
             ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
         )
         download_queue_service = DownloadQueueService(event_bus=events)
-        model_metadata_service = ModelMetadataStoreSQL(db=db)
+        model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")
         model_manager = ModelManagerService.build_model_manager(
             app_config=configuration,
-            model_record_service=ModelRecordServiceSQL(db=db, metadata_store=model_metadata_service),
+            model_record_service=ModelRecordServiceSQL(db=db),
             download_queue=download_queue_service,
             events=events,
         )
@@ -120,6 +122,7 @@ class ApiDependencies:
             images=images,
             invocation_cache=invocation_cache,
             logger=logger,
+            model_images=model_images_service,
             model_manager=model_manager,
             download_queue=download_queue_service,
             names=names,

invokeai/app/api/routers/model_manager.py

@@ -1,28 +1,26 @@
 # Copyright (c) 2023 Lincoln D. Stein
 """FastAPI route for model configuration records."""
 
+import io
 import pathlib
 import shutil
-from hashlib import sha1
-from random import randbytes
-from typing import Any, Dict, List, Optional, Set
+import traceback
+from typing import Any, Dict, List, Optional
 
-from fastapi import Body, Path, Query, Response
+from fastapi import Body, Path, Query, Response, UploadFile
+from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
+from PIL import Image
 from pydantic import BaseModel, ConfigDict, Field
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
 from invokeai.app.services.model_install import ModelInstallJob
-from invokeai.app.services.model_metadata.metadata_store_base import ModelMetadataChanges
 from invokeai.app.services.model_records import (
-    DuplicateModelException,
     InvalidModelException,
-    ModelRecordOrderBy,
-    ModelSummary,
     UnknownModelException,
 )
-from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.app.services.model_records.model_records_base import DuplicateModelException, ModelRecordChanges
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
@@ -31,15 +29,15 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.merge import MergeInterpolationMethod, ModelMerger
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
-from invokeai.backend.model_manager.metadata.metadata_base import BaseMetadata
 from invokeai.backend.model_manager.search import ModelSearch
 
 from ..dependencies import ApiDependencies
 
 model_manager_router = APIRouter(prefix="/v2/models", tags=["model_manager"])
 
+# images are immutable; set a high max-age
+IMAGE_MAX_AGE = 31536000
+
 
 class ModelsList(BaseModel):
     """Return list of configs."""
@@ -49,15 +47,6 @@ class ModelsList(BaseModel):
     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.
@@ -68,19 +57,16 @@ example_model_config = {
     "base": "sd-1",
     "type": "main",
     "format": "checkpoint",
-    "config": "string",
+    "config_path": "string",
     "key": "string",
-    "original_hash": "string",
-    "current_hash": "string",
+    "hash": "string",
     "description": "string",
     "source": "string",
-    "last_modified": 0,
-    "vae": "string",
+    "converted_at": 0,
     "variant": "normal",
     "prediction_type": "epsilon",
     "repo_variant": "fp16",
     "upcast_attention": False,
-    "ztsnr_training": False,
 }
 
 example_model_input = {
@@ -89,50 +75,12 @@ example_model_input = {
     "base": "sd-1",
     "type": "main",
     "format": "checkpoint",
-    "config": "configs/stable-diffusion/v1-inference.yaml",
+    "config_path": "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
 ##############################################################################
@@ -164,6 +112,9 @@ async def list_model_records(
         found_models.extend(
             record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
         )
+    for model in found_models:
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(model.key)
+        model.cover_image = cover_image
     return ModelsList(models=found_models)
 
 
@@ -207,94 +158,23 @@ async def get_model_record(
     record_store = ApiDependencies.invoker.services.model_manager.store
     try:
         config: AnyModelConfig = record_store.get_model(key)
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(key)
+        config.cover_image = cover_image
         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(
-    "/i/{key}/metadata",
-    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"},
-    },
-)
-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)
-
-    return result
-
-
-@model_manager_router.patch(
-    "/i/{key}/metadata",
-    operation_id="update_model_metadata",
-    responses={
-        201: {
-            "description": "The model metadata was updated successfully",
-            "content": {"application/json": {"example": example_model_metadata}},
-        },
-        400: {"description": "Bad request"},
-    },
-)
-async def update_model_metadata(
-    key: str = Path(description="Key of the model repo metadata to fetch."),
-    changes: ModelMetadataChanges = Body(description="The changes"),
-) -> Optional[AnyModelRepoMetadata]:
-    """Updates or creates a model metadata object."""
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    metadata_store = ApiDependencies.invoker.services.model_manager.store.metadata_store
-
-    try:
-        original_metadata = record_store.get_metadata(key)
-        if original_metadata:
-            if changes.default_settings:
-                original_metadata.default_settings = changes.default_settings
-
-            metadata_store.update_metadata(key, original_metadata)
-        else:
-            metadata_store.add_metadata(
-                key, BaseMetadata(name="", author="", default_settings=changes.default_settings)
-            )
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"An error occurred while updating the model metadata: {e}",
-        )
-
-    result: Optional[AnyModelRepoMetadata] = record_store.get_metadata(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("/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
 
 
 class FoundModel(BaseModel):
@@ -366,19 +246,6 @@ async def scan_for_models(
     return scan_results
 
 
-@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",
@@ -395,15 +262,13 @@ async def search_by_metadata_tags(
 )
 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)
-    ],
+    changes: Annotated[ModelRecordChanges, Body(description="Model config", 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."""
+    """Update a model's config."""
     logger = ApiDependencies.invoker.services.logger
     record_store = ApiDependencies.invoker.services.model_manager.store
     try:
-        model_response: AnyModelConfig = record_store.update_model(key, config=info)
+        model_response: AnyModelConfig = record_store.update_model(key, changes=changes)
         logger.info(f"Updated model: {key}")
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
@@ -413,16 +278,85 @@ async def update_model_record(
     return model_response
 
 
+@model_manager_router.get(
+    "/i/{key}/image",
+    operation_id="get_model_image",
+    responses={
+        200: {
+            "description": "The model image was fetched successfully",
+        },
+        400: {"description": "Bad request"},
+        404: {"description": "The model image could not be found"},
+    },
+    status_code=200,
+)
+async def get_model_image(
+    key: str = Path(description="The name of model image file to get"),
+) -> FileResponse:
+    """Gets an image file that previews the model"""
+
+    try:
+        path = ApiDependencies.invoker.services.model_images.get_path(key)
+
+        response = FileResponse(
+            path,
+            media_type="image/png",
+            filename=key + ".png",
+            content_disposition_type="inline",
+        )
+        response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
+        return response
+    except Exception:
+        raise HTTPException(status_code=404)
+
+
+@model_manager_router.patch(
+    "/i/{key}/image",
+    operation_id="update_model_image",
+    responses={
+        200: {
+            "description": "The model image was updated successfully",
+        },
+        400: {"description": "Bad request"},
+    },
+    status_code=200,
+)
+async def update_model_image(
+    key: Annotated[str, Path(description="Unique key of model")],
+    image: UploadFile,
+) -> None:
+    if not image.content_type or not image.content_type.startswith("image"):
+        raise HTTPException(status_code=415, detail="Not an image")
+
+    contents = await image.read()
+    try:
+        pil_image = Image.open(io.BytesIO(contents))
+
+    except Exception:
+        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
+        raise HTTPException(status_code=415, detail="Failed to read image")
+
+    logger = ApiDependencies.invoker.services.logger
+    model_images = ApiDependencies.invoker.services.model_images
+    try:
+        model_images.save(pil_image, key)
+        logger.info(f"Updated image for model: {key}")
+    except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+    return
+
+
 @model_manager_router.delete(
     "/i/{key}",
-    operation_id="del_model_record",
+    operation_id="delete_model",
     responses={
         204: {"description": "Model deleted successfully"},
         404: {"description": "Model not found"},
     },
     status_code=204,
 )
-async def del_model_record(
+async def delete_model(
     key: str = Path(description="Unique key of model to remove from model registry."),
 ) -> Response:
     """
@@ -443,42 +377,62 @@ async def del_model_record(
         raise HTTPException(status_code=404, detail=str(e))
 
 
-@model_manager_router.post(
-    "/i/",
-    operation_id="add_model_record",
+@model_manager_router.delete(
+    "/i/{key}/image",
+    operation_id="delete_model_image",
     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"},
+        204: {"description": "Model image deleted successfully"},
+        404: {"description": "Model image not found"},
     },
-    status_code=201,
+    status_code=204,
 )
-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."""
+async def delete_model_image(
+    key: str = Path(description="Unique key of model image to remove from model_images directory."),
+) -> None:
     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}")
+    model_images = ApiDependencies.invoker.services.model_images
     try:
-        record_store.add_model(config.key, config)
-    except DuplicateModelException as e:
+        model_images.delete(key)
+        logger.info(f"Deleted model image: {key}")
+        return
+    except UnknownModelException 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)
+        raise HTTPException(status_code=404, detail=str(e))
 
-    # now fetch it out
-    result: AnyModelConfig = record_store.get_model(config.key)
-    return result
+
+# @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
+#     try:
+#         record_store.add_model(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(
@@ -553,10 +507,10 @@ async def install_model(
 
 
 @model_manager_router.get(
-    "/import",
-    operation_id="list_model_install_jobs",
+    "/install",
+    operation_id="list_model_installs",
 )
-async def list_model_install_jobs() -> List[ModelInstallJob]:
+async def list_model_installs() -> List[ModelInstallJob]:
     """Return the list of model install jobs.
 
     Install jobs have a numeric `id`, a `status`, and other fields that provide information on
@@ -570,9 +524,8 @@ async def list_model_install_jobs() -> List[ModelInstallJob]:
     * "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`.
+    model and type 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.
     """
@@ -581,7 +534,7 @@ async def list_model_install_jobs() -> List[ModelInstallJob]:
 
 
 @model_manager_router.get(
-    "/import/{id}",
+    "/install/{id}",
     operation_id="get_model_install_job",
     responses={
         200: {"description": "Success"},
@@ -601,7 +554,7 @@ async def get_model_install_job(id: int = Path(description="Model install id"))
 
 
 @model_manager_router.delete(
-    "/import/{id}",
+    "/install/{id}",
     operation_id="cancel_model_install_job",
     responses={
         201: {"description": "The job was cancelled successfully"},
@@ -619,8 +572,8 @@ async def cancel_model_install_job(id: int = Path(description="Model install job
     installer.cancel_job(job)
 
 
-@model_manager_router.patch(
-    "/import",
+@model_manager_router.delete(
+    "/install",
     operation_id="prune_model_install_jobs",
     responses={
         204: {"description": "All completed and errored jobs have been pruned"},
@@ -690,7 +643,7 @@ async def convert_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
-    model_manager.load_model_by_config(model_config, submodel_type=SubModelType.Scheduler)
+    model_manager.load.load_model(model_config, submodel_type=SubModelType.Scheduler)
 
     # Get the path of the converted model from the loader
     cache_path = loader.convert_cache.cache_path(key)
@@ -699,7 +652,8 @@ async def convert_model(
     # 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)
+    changes = ModelRecordChanges(name=model_config.name)
+    store.update_model(key, changes=changes)
 
     # install the diffusers
     try:
@@ -708,7 +662,7 @@ async def convert_model(
             config={
                 "name": original_name,
                 "description": model_config.description,
-                "original_hash": model_config.original_hash,
+                "hash": model_config.hash,
                 "source": model_config.source,
             },
         )
@@ -716,10 +670,6 @@ async def convert_model(
         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)
 
@@ -731,66 +681,66 @@ async def convert_model(
     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
+# @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_app.py

@@ -2,12 +2,9 @@
 # which are imported/used before parse_args() is called will get the default config values instead of the
 # values from the command line or config file.
 import sys
-from contextlib import asynccontextmanager
 
-from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.version.invokeai_version import __version__
 
-from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 from .services.config import InvokeAIAppConfig
 
 app_config = InvokeAIAppConfig.get_config()
@@ -20,6 +17,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     import asyncio
     import mimetypes
     import socket
+    from contextlib import asynccontextmanager
     from inspect import signature
     from pathlib import Path
     from typing import Any
@@ -40,6 +38,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     # noinspection PyUnresolvedReferences
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
     import invokeai.frontend.web as web_dir
+    from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 
     from ..backend.util.logging import InvokeAILogger
     from .api.dependencies import ApiDependencies
@@ -59,6 +58,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         BaseInvocation,
         UIConfigBase,
     )
+    from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 
     if is_mps_available():
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)

invokeai/app/invocations/compel.py

@@ -20,7 +20,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 from invokeai.backend.util.devices import torch_dtype
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from .model import ClipField
+from .model import CLIPField
 
 # unconditioned: Optional[torch.Tensor]
 
@@ -46,7 +46,7 @@ class CompelInvocation(BaseInvocation):
         description=FieldDescriptions.compel_prompt,
         ui_component=UIComponent.Textarea,
     )
-    clip: ClipField = InputField(
+    clip: CLIPField = InputField(
         title="CLIP",
         description=FieldDescriptions.clip,
         input=Input.Connection,
@@ -54,16 +54,16 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
+        tokenizer_info = context.models.load(self.clip.tokenizer)
         tokenizer_model = tokenizer_info.model
         assert isinstance(tokenizer_model, CLIPTokenizer)
-        text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
+        text_encoder_info = context.models.load(self.clip.text_encoder)
         text_encoder_model = text_encoder_info.model
         assert isinstance(text_encoder_model, CLIPTextModel)
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.clip.loras:
-                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
+                lora_info = context.models.load(lora.lora)
                 assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
@@ -127,16 +127,16 @@ class SDXLPromptInvocationBase:
     def run_clip_compel(
         self,
         context: InvocationContext,
-        clip_field: ClipField,
+        clip_field: CLIPField,
         prompt: str,
         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())
+        tokenizer_info = context.models.load(clip_field.tokenizer)
         tokenizer_model = tokenizer_info.model
         assert isinstance(tokenizer_model, CLIPTokenizer)
-        text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
+        text_encoder_info = context.models.load(clip_field.text_encoder)
         text_encoder_model = text_encoder_info.model
         assert isinstance(text_encoder_model, (CLIPTextModel, CLIPTextModelWithProjection))
 
@@ -163,7 +163,7 @@ class SDXLPromptInvocationBase:
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in clip_field.loras:
-                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
+                lora_info = context.models.load(lora.lora)
                 lora_model = lora_info.model
                 assert isinstance(lora_model, LoRAModelRaw)
                 yield (lora_model, lora.weight)
@@ -253,8 +253,8 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     crop_left: int = InputField(default=0, description="")
     target_width: int = InputField(default=1024, description="")
     target_height: int = InputField(default=1024, description="")
-    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
-    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
+    clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
+    clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -340,7 +340,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
     crop_top: int = InputField(default=0, description="")
     crop_left: int = InputField(default=0, description="")
     aesthetic_score: float = InputField(default=6.0, description=FieldDescriptions.sdxl_aesthetic)
-    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
+    clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -370,10 +370,10 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 
 
 @invocation_output("clip_skip_output")
-class ClipSkipInvocationOutput(BaseInvocationOutput):
-    """Clip skip node output"""
+class CLIPSkipInvocationOutput(BaseInvocationOutput):
+    """CLIP skip node output"""
 
-    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
+    clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
 @invocation(
@@ -383,15 +383,15 @@ class ClipSkipInvocationOutput(BaseInvocationOutput):
     category="conditioning",
     version="1.0.0",
 )
-class ClipSkipInvocation(BaseInvocation):
+class CLIPSkipInvocation(BaseInvocation):
     """Skip layers in clip text_encoder model."""
 
-    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
+    clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
     skipped_layers: int = InputField(default=0, ge=0, description=FieldDescriptions.skipped_layers)
 
-    def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
+    def invoke(self, context: InvocationContext) -> CLIPSkipInvocationOutput:
         self.clip.skipped_layers += self.skipped_layers
-        return ClipSkipInvocationOutput(
+        return CLIPSkipInvocationOutput(
             clip=self.clip,
         )
 

invokeai/app/invocations/controlnet_image_processors.py

@@ -34,6 +34,7 @@ from invokeai.app.invocations.fields import (
     WithBoard,
     WithMetadata,
 )
+from invokeai.app.invocations.model import ModelField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -51,15 +52,9 @@ CONTROLNET_RESIZE_VALUES = Literal[
 ]
 
 
-class ControlNetModelField(BaseModel):
-    """ControlNet model field"""
-
-    key: str = Field(description="Model config record key for the ControlNet model")
-
-
 class ControlField(BaseModel):
     image: ImageField = Field(description="The control image")
-    control_model: ControlNetModelField = Field(description="The ControlNet model to use")
+    control_model: ModelField = Field(description="The ControlNet model to use")
     control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
@@ -95,7 +90,7 @@ class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
-    control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
+    control_model: ModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
     control_weight: Union[float, List[float]] = InputField(
         default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
     )

invokeai/app/invocations/fields.py

@@ -228,7 +228,7 @@ class ConditioningField(BaseModel):
     # endregion
 
 
-class MetadataField(RootModel):
+class MetadataField(RootModel[dict[str, Any]]):
     """
     Pydantic model for metadata with custom root of type dict[str, Any].
     Metadata is stored without a strict schema.

invokeai/app/invocations/ip_adapter.py

@@ -11,25 +11,17 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
+from invokeai.app.invocations.model import ModelField
 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
-
-
-# LS: Consider moving these two classes into model.py
-class IPAdapterModelField(BaseModel):
-    key: str = Field(description="Key to the IP-Adapter model")
-
-
-class CLIPVisionModelField(BaseModel):
-    key: str = Field(description="Key to the CLIP Vision image encoder model")
+from invokeai.backend.model_manager.config import BaseModelType, IPAdapterConfig, ModelType
 
 
 class IPAdapterField(BaseModel):
     image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
-    image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
+    ip_adapter_model: ModelField = Field(description="The IP-Adapter model to use.")
+    image_encoder_model: ModelField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
@@ -62,7 +54,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: IPAdapterModelField = InputField(
+    ip_adapter_model: ModelField = InputField(
         description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
     )
 
@@ -90,18 +82,18 @@ class IPAdapterInvocation(BaseInvocation):
     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)
+        assert isinstance(ip_adapter_info, IPAdapterConfig)
         image_encoder_model_id = ip_adapter_info.image_encoder_model_id
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         image_encoder_models = context.models.search_by_attrs(
             name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
         )
         assert len(image_encoder_models) == 1
-        image_encoder_model = CLIPVisionModelField(key=image_encoder_models[0].key)
         return IPAdapterOutput(
             ip_adapter=IPAdapterField(
                 image=self.image,
                 ip_adapter_model=self.ip_adapter_model,
-                image_encoder_model=image_encoder_model,
+                image_encoder_model=ModelField(key=image_encoder_models[0].key),
                 weight=self.weight,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,

invokeai/app/invocations/latent.py

@@ -26,6 +26,7 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 from PIL import Image, ImageFilter
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
+from transformers import CLIPVisionModelWithProjection
 
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
 from invokeai.app.invocations.fields import (
@@ -75,7 +76,7 @@ from .baseinvocation import (
     invocation_output,
 )
 from .controlnet_image_processors import ControlField
-from .model import ModelInfo, UNetField, VaeField
+from .model import ModelField, UNetField, VAEField
 
 if choose_torch_device() == torch.device("mps"):
     from torch import mps
@@ -118,7 +119,7 @@ class SchedulerInvocation(BaseInvocation):
 class CreateDenoiseMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
 
-    vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
+    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
     image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
     mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
@@ -153,7 +154,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
         if image_tensor is not None:
-            vae_info = context.models.load(**self.vae.vae.model_dump())
+            vae_info = context.models.load(self.vae.vae)
 
             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)
@@ -244,12 +245,12 @@ class CreateGradientMaskInvocation(BaseInvocation):
 
 def get_scheduler(
     context: InvocationContext,
-    scheduler_info: ModelInfo,
+    scheduler_info: ModelField,
     scheduler_name: str,
     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.models.load(scheduler_info)
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -461,7 +462,7 @@ 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.models.load(control_info.control_model))
 
             # control_models.append(control_model)
             control_image_field = control_info.image
@@ -523,11 +524,10 @@ 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.models.load(single_ip_adapter.ip_adapter_model)
             )
 
-            image_encoder_model_info = context.models.load(key=single_ip_adapter.image_encoder_model.key)
-
+            image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
             # `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):
@@ -538,6 +538,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             # 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.
             with image_encoder_model_info as image_encoder_model:
+                assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
                 # Get image embeddings from CLIP and ImageProjModel.
                 image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
                     single_ipa_images, image_encoder_model
@@ -577,8 +578,8 @@ 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)
+            t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
+            t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
             image = context.images.get_pil(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
@@ -731,12 +732,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
                 for lora in self.unet.loras:
-                    lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
+                    lora_info = context.models.load(lora.lora)
+                    assert isinstance(lora_info.model, LoRAModelRaw)
                     yield (lora_info.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.models.load(**self.unet.unet.model_dump())
+            unet_info = context.models.load(self.unet.unet)
             assert isinstance(unet_info.model, UNet2DConditionModel)
             with (
                 ExitStack() as exit_stack,
@@ -830,7 +832,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         description=FieldDescriptions.latents,
         input=Input.Connection,
     )
-    vae: VaeField = InputField(
+    vae: VAEField = InputField(
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
@@ -841,8 +843,8 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        vae_info = context.models.load(**self.vae.vae.model_dump())
-
+        vae_info = context.models.load(self.vae.vae)
+        assert isinstance(vae_info.model, (UNet2DConditionModel, AutoencoderKL))
         with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             assert isinstance(vae, torch.nn.Module)
             latents = latents.to(vae.device)
@@ -1008,7 +1010,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     image: ImageField = InputField(
         description="The image to encode",
     )
-    vae: VaeField = InputField(
+    vae: VAEField = InputField(
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
@@ -1064,7 +1066,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         image = context.images.get_pil(self.image.image_name)
 
-        vae_info = context.models.load(**self.vae.vae.model_dump())
+        vae_info = context.models.load(self.vae.vae)
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:

invokeai/app/invocations/metadata.py

@@ -8,7 +8,10 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.controlnet_image_processors import ControlField
+from invokeai.app.invocations.controlnet_image_processors import (
+    CONTROLNET_MODE_VALUES,
+    CONTROLNET_RESIZE_VALUES,
+)
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -17,10 +20,8 @@ from invokeai.app.invocations.fields import (
     OutputField,
     UIType,
 )
-from invokeai.app.invocations.ip_adapter import IPAdapterModelField
-from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
-from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
 
 from ...version import __version__
 
@@ -30,10 +31,20 @@ class MetadataItemField(BaseModel):
     value: Any = Field(description=FieldDescriptions.metadata_item_value)
 
 
+class ModelMetadataField(BaseModel):
+    """Model Metadata Field"""
+
+    key: str
+    hash: str
+    name: str
+    base: BaseModelType
+    type: ModelType
+
+
 class LoRAMetadataField(BaseModel):
     """LoRA Metadata Field"""
 
-    model: LoRAModelField = Field(description=FieldDescriptions.lora_model)
+    model: ModelMetadataField = Field(description=FieldDescriptions.lora_model)
     weight: float = Field(description=FieldDescriptions.lora_weight)
 
 
@@ -41,7 +52,7 @@ class IPAdapterMetadataField(BaseModel):
     """IP Adapter Field, minus the CLIP Vision Encoder model"""
 
     image: ImageField = Field(description="The IP-Adapter image prompt.")
-    ip_adapter_model: IPAdapterModelField = Field(
+    ip_adapter_model: ModelMetadataField = Field(
         description="The IP-Adapter model.",
     )
     weight: Union[float, list[float]] = Field(
@@ -51,6 +62,33 @@ class IPAdapterMetadataField(BaseModel):
     end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
 
 
+class T2IAdapterMetadataField(BaseModel):
+    image: ImageField = Field(description="The T2I-Adapter image prompt.")
+    t2i_adapter_model: ModelMetadataField = Field(description="The T2I-Adapter model to use.")
+    weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+
+class ControlNetMetadataField(BaseModel):
+    image: ImageField = Field(description="The control image")
+    control_model: ModelMetadataField = Field(description="The ControlNet model to use")
+    control_weight: Union[float, list[float]] = Field(default=1, description="The weight given to the ControlNet")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+
 @invocation_output("metadata_item_output")
 class MetadataItemOutput(BaseInvocationOutput):
     """Metadata Item Output"""
@@ -140,14 +178,14 @@ class CoreMetadataInvocation(BaseInvocation):
         default=None,
         description="The number of skipped CLIP layers",
     )
-    model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference")
-    controlnets: Optional[list[ControlField]] = InputField(
+    model: Optional[ModelMetadataField] = InputField(default=None, description="The main model used for inference")
+    controlnets: Optional[list[ControlNetMetadataField]] = InputField(
         default=None, description="The ControlNets used for inference"
     )
     ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
         default=None, description="The IP Adapters used for inference"
     )
-    t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
+    t2iAdapters: Optional[list[T2IAdapterMetadataField]] = InputField(
         default=None, description="The IP Adapters used for inference"
     )
     loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
@@ -159,7 +197,7 @@ class CoreMetadataInvocation(BaseInvocation):
         default=None,
         description="The name of the initial image",
     )
-    vae: Optional[VAEModelField] = InputField(
+    vae: Optional[ModelMetadataField] = InputField(
         default=None,
         description="The VAE used for decoding, if the main model's default was not used",
     )
@@ -190,7 +228,7 @@ class CoreMetadataInvocation(BaseInvocation):
     )
 
     # SDXL Refiner
-    refiner_model: Optional[MainModelField] = InputField(
+    refiner_model: Optional[ModelMetadataField] = InputField(
         default=None,
         description="The SDXL Refiner model used",
     )
@@ -222,10 +260,9 @@ class CoreMetadataInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> MetadataOutput:
         """Collects and outputs a CoreMetadata object"""
 
-        return MetadataOutput(
-            metadata=MetadataField.model_validate(
-                self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
-            )
-        )
+        as_dict = self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
+        as_dict["app_version"] = __version__
+
+        return MetadataOutput(metadata=MetadataField.model_validate(as_dict))
 
     model_config = ConfigDict(extra="allow")

invokeai/app/invocations/model.py

@@ -6,8 +6,8 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
+from invokeai.backend.model_manager.config import SubModelType
 
-from ...backend.model_manager import SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -16,33 +16,33 @@ from .baseinvocation import (
 )
 
 
-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")
+class ModelField(BaseModel):
+    key: str = Field(description="Key of the model")
+    submodel_type: Optional[SubModelType] = Field(description="Submodel type", default=None)
 
 
-class LoraInfo(ModelInfo):
-    weight: float = Field(description="Lora's weight which to use when apply to model")
+class LoRAField(BaseModel):
+    lora: ModelField = Field(description="Info to load lora model")
+    weight: float = Field(description="Weight to apply to lora model")
 
 
 class UNetField(BaseModel):
-    unet: ModelInfo = Field(description="Info to load unet submodel")
-    scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
-    loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
+    unet: ModelField = Field(description="Info to load unet submodel")
+    scheduler: ModelField = Field(description="Info to load scheduler submodel")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
     freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
 
 
-class ClipField(BaseModel):
-    tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
-    text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
+class CLIPField(BaseModel):
+    tokenizer: ModelField = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelField = Field(description="Info to load text_encoder submodel")
     skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
-    loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
-class VaeField(BaseModel):
-    # TODO: better naming?
-    vae: ModelInfo = Field(description="Info to load vae submodel")
+class VAEField(BaseModel):
+    vae: ModelField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
@@ -57,14 +57,14 @@ class UNetOutput(BaseInvocationOutput):
 class VAEOutput(BaseInvocationOutput):
     """Base class for invocations that output a VAE field"""
 
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
 @invocation_output("clip_output")
 class CLIPOutput(BaseInvocationOutput):
     """Base class for invocations that output a CLIP field"""
 
-    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
 
 
 @invocation_output("model_loader_output")
@@ -74,18 +74,6 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
     pass
 
 
-class MainModelField(BaseModel):
-    """Main model field"""
-
-    key: str = Field(description="Model key")
-
-
-class LoRAModelField(BaseModel):
-    """LoRA model field"""
-
-    key: str = Field(description="LoRA model key")
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",
@@ -96,62 +84,40 @@ class LoRAModelField(BaseModel):
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
+    model: ModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
-        key = self.model.key
-
         # TODO: not found exceptions
-        if not context.models.exists(key):
-            raise Exception(f"Unknown model {key}")
+        if not context.models.exists(self.model.key):
+            raise Exception(f"Unknown model {self.model.key}")
+
+        unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
+        scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
+        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
 
         return ModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip=ClipField(
-                tokenizer=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Tokenizer,
-                ),
-                text_encoder=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.TextEncoder,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae=VaeField(
-                vae=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Vae,
-                ),
-            ),
+            unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
+            vae=VAEField(vae=vae),
         )
 
 
 @invocation_output("lora_loader_output")
-class LoraLoaderOutput(BaseInvocationOutput):
+class LoRALoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
     unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
-    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
+    clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
 @invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
-class LoraLoaderInvocation(BaseInvocation):
+class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
-    lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
+    lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -159,46 +125,41 @@ class LoraLoaderInvocation(BaseInvocation):
         input=Input.Connection,
         title="UNet",
     )
-    clip: Optional[ClipField] = InputField(
+    clip: Optional[CLIPField] = InputField(
         default=None,
         description=FieldDescriptions.clip,
         input=Input.Connection,
         title="CLIP",
     )
 
-    def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
-        if self.lora is None:
-            raise Exception("No LoRA provided")
-
+    def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
         lora_key = self.lora.key
 
         if not context.models.exists(lora_key):
             raise Exception(f"Unkown lora: {lora_key}!")
 
-        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.lora.key == lora_key for lora in self.unet.loras):
+            raise Exception(f'LoRA "{lora_key}" 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.lora.key == lora_key for lora in self.clip.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to clip')
 
-        output = LoraLoaderOutput()
+        output = LoRALoaderOutput()
 
         if self.unet is not None:
-            output.unet = copy.deepcopy(self.unet)
+            output.unet = self.unet.model_copy(deep=True)
             output.unet.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
 
         if self.clip is not None:
-            output.clip = copy.deepcopy(self.clip)
+            output.clip = self.clip.model_copy(deep=True)
             output.clip.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
@@ -207,12 +168,12 @@ class LoraLoaderInvocation(BaseInvocation):
 
 
 @invocation_output("sdxl_lora_loader_output")
-class SDXLLoraLoaderOutput(BaseInvocationOutput):
+class SDXLLoRALoaderOutput(BaseInvocationOutput):
     """SDXL LoRA Loader Output"""
 
     unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
-    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
-    clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
+    clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
+    clip2: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
 
 
 @invocation(
@@ -222,10 +183,10 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
     category="model",
     version="1.0.1",
 )
-class SDXLLoraLoaderInvocation(BaseInvocation):
+class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
-    lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
+    lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -233,65 +194,59 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
         input=Input.Connection,
         title="UNet",
     )
-    clip: Optional[ClipField] = InputField(
+    clip: Optional[CLIPField] = InputField(
         default=None,
         description=FieldDescriptions.clip,
         input=Input.Connection,
         title="CLIP 1",
     )
-    clip2: Optional[ClipField] = InputField(
+    clip2: Optional[CLIPField] = InputField(
         default=None,
         description=FieldDescriptions.clip,
         input=Input.Connection,
         title="CLIP 2",
     )
 
-    def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
-        if self.lora is None:
-            raise Exception("No LoRA provided")
-
+    def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
         lora_key = self.lora.key
 
         if not context.models.exists(lora_key):
             raise Exception(f"Unknown lora: {lora_key}!")
 
-        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.lora.key == lora_key for lora in self.unet.loras):
+            raise Exception(f'LoRA "{lora_key}" 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.lora.key == lora_key for lora in self.clip.loras):
+            raise Exception(f'LoRA "{lora_key}" 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.lora.key == lora_key for lora in self.clip2.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to clip2')
 
-        output = SDXLLoraLoaderOutput()
+        output = SDXLLoRALoaderOutput()
 
         if self.unet is not None:
-            output.unet = copy.deepcopy(self.unet)
+            output.unet = self.unet.model_copy(deep=True)
             output.unet.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
 
         if self.clip is not None:
-            output.clip = copy.deepcopy(self.clip)
+            output.clip = self.clip.model_copy(deep=True)
             output.clip.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
 
         if self.clip2 is not None:
-            output.clip2 = copy.deepcopy(self.clip2)
+            output.clip2 = self.clip2.model_copy(deep=True)
             output.clip2.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
@@ -299,17 +254,11 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
         return output
 
 
-class VAEModelField(BaseModel):
-    """Vae model field"""
-
-    key: str = Field(description="Model's key")
-
-
 @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
-class VaeLoaderInvocation(BaseInvocation):
+class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
-    vae_model: VAEModelField = InputField(
+    vae_model: ModelField = InputField(
         description=FieldDescriptions.vae_model,
         input=Input.Direct,
         title="VAE",
@@ -321,7 +270,7 @@ class VaeLoaderInvocation(BaseInvocation):
         if not context.models.exists(key):
             raise Exception(f"Unkown vae: {key}!")
 
-        return VAEOutput(vae=VaeField(vae=ModelInfo(key=key)))
+        return VAEOutput(vae=VAEField(vae=self.vae_model))
 
 
 @invocation_output("seamless_output")
@@ -329,7 +278,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
     """Modified Seamless Model output"""
 
     unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
-    vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
+    vae: Optional[VAEField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
 
 
 @invocation(
@@ -348,7 +297,7 @@ class SeamlessModeInvocation(BaseInvocation):
         input=Input.Connection,
         title="UNet",
     )
-    vae: Optional[VaeField] = InputField(
+    vae: Optional[VAEField] = InputField(
         default=None,
         description=FieldDescriptions.vae_model,
         input=Input.Connection,

invokeai/app/invocations/sdxl.py

@@ -8,7 +8,7 @@ from .baseinvocation import (
     invocation,
     invocation_output,
 )
-from .model import ClipField, MainModelField, ModelInfo, UNetField, VaeField
+from .model import CLIPField, ModelField, UNetField, VAEField
 
 
 @invocation_output("sdxl_model_loader_output")
@@ -16,9 +16,9 @@ class SDXLModelLoaderOutput(BaseInvocationOutput):
     """SDXL base model loader output"""
 
     unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 1")
-    clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 1")
+    clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
 @invocation_output("sdxl_refiner_model_loader_output")
@@ -26,15 +26,15 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     """SDXL refiner model loader output"""
 
     unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-    clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
+    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")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
-    model: MainModelField = InputField(
+    model: ModelField = InputField(
         description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
     )
     # TODO: precision?
@@ -46,48 +46,19 @@ class SDXLModelLoaderInvocation(BaseInvocation):
         if not context.models.exists(model_key):
             raise Exception(f"Unknown model: {model_key}")
 
+        unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
+        scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
+        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        tokenizer2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        text_encoder2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+
         return SDXLModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip=ClipField(
-                tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer,
-                ),
-                text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            clip2=ClipField(
-                tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer2,
-                ),
-                text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder2,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae=VaeField(
-                vae=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Vae,
-                ),
-            ),
+            unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
+            clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
+            vae=VAEField(vae=vae),
         )
 
 
@@ -101,10 +72,8 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
 
-    model: MainModelField = InputField(
-        description=FieldDescriptions.sdxl_refiner_model,
-        input=Input.Direct,
-        ui_type=UIType.SDXLRefinerModel,
+    model: ModelField = InputField(
+        description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
     )
     # TODO: precision?
 
@@ -115,34 +84,14 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
         if not context.models.exists(model_key):
             raise Exception(f"Unknown model: {model_key}")
 
+        unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
+        scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
+        tokenizer2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        text_encoder2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+
         return SDXLRefinerModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip2=ClipField(
-                tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer2,
-                ),
-                text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder2,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae=VaeField(
-                vae=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Vae,
-                ),
-            ),
+            unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
+            clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
+            vae=VAEField(vae=vae),
         )

invokeai/app/invocations/t2i_adapter.py

@@ -10,17 +10,14 @@ from invokeai.app.invocations.baseinvocation import (
 )
 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.model import ModelField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
 
 
-class T2IAdapterModelField(BaseModel):
-    key: str = Field(description="Model record key for the T2I-Adapter model")
-
-
 class T2IAdapterField(BaseModel):
     image: ImageField = Field(description="The T2I-Adapter image prompt.")
-    t2i_adapter_model: T2IAdapterModelField = Field(description="The T2I-Adapter model to use.")
+    t2i_adapter_model: ModelField = Field(description="The T2I-Adapter model to use.")
     weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
@@ -55,7 +52,7 @@ class T2IAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: ImageField = InputField(description="The IP-Adapter image prompt.")
-    t2i_adapter_model: T2IAdapterModelField = InputField(
+    t2i_adapter_model: ModelField = InputField(
         description="The T2I-Adapter model.",
         title="T2I-Adapter Model",
         input=Input.Direct,

invokeai/app/services/config/config_default.py

@@ -256,6 +256,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     profile_graphs      : bool = Field(default=False, description="Enable graph profiling", json_schema_extra=Categories.Development)
     profile_prefix      : Optional[str] = Field(default=None, description="An optional prefix for profile output files.", json_schema_extra=Categories.Development)
     profiles_dir        : Path = Field(default=Path('profiles'), description="Directory for graph profiles", json_schema_extra=Categories.Development)
+    skip_model_hash     : bool = Field(default=False, description="Skip model hashing, instead assigning a UUID to models. Useful when using a memory db to reduce startup time.", json_schema_extra=Categories.Development)
 
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
 

invokeai/app/services/invocation_cache/invocation_cache_base.py

@@ -41,8 +41,9 @@ class InvocationCacheBase(ABC):
         """Clears the cache"""
         pass
 
+    @staticmethod
     @abstractmethod
-    def create_key(self, invocation: BaseInvocation) -> int:
+    def create_key(invocation: BaseInvocation) -> int:
         """Gets the key for the invocation's cache item"""
         pass
 

invokeai/app/services/invocation_cache/invocation_cache_memory.py

@@ -61,9 +61,7 @@ class MemoryInvocationCache(InvocationCacheBase):
             self._delete_oldest_access(number_to_delete)
             self._cache[key] = CachedItem(
                 invocation_output,
-                invocation_output.model_dump_json(
-                    warnings=False, exclude_defaults=True, exclude_unset=True, include={"type"}
-                ),
+                invocation_output.model_dump_json(warnings=False, exclude_defaults=True, exclude_unset=True),
             )
 
     def _delete_oldest_access(self, number_to_delete: int) -> None:
@@ -81,7 +79,7 @@ class MemoryInvocationCache(InvocationCacheBase):
         with self._lock:
             return self._delete(key)
 
-    def clear(self, *args, **kwargs) -> None:
+    def clear(self) -> None:
         with self._lock:
             if self._max_cache_size == 0:
                 return

invokeai/app/services/invocation_services.py

@@ -25,6 +25,7 @@ if TYPE_CHECKING:
     from .images.images_base import ImageServiceABC
     from .invocation_cache.invocation_cache_base import InvocationCacheBase
     from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
+    from .model_images.model_images_base import ModelImageFileStorageBase
     from .model_manager.model_manager_base import ModelManagerServiceBase
     from .names.names_base import NameServiceBase
     from .session_processor.session_processor_base import SessionProcessorBase
@@ -49,6 +50,7 @@ class InvocationServices:
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
         logger: "Logger",
+        model_images: "ModelImageFileStorageBase",
         model_manager: "ModelManagerServiceBase",
         download_queue: "DownloadQueueServiceBase",
         performance_statistics: "InvocationStatsServiceBase",
@@ -72,6 +74,7 @@ class InvocationServices:
         self.image_files = image_files
         self.image_records = image_records
         self.logger = logger
+        self.model_images = model_images
         self.model_manager = model_manager
         self.download_queue = download_queue
         self.performance_statistics = performance_statistics

invokeai/app/services/model_images/model_images_base.py

@@ -0,0 +1,33 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from PIL.Image import Image as PILImageType
+
+
+class ModelImageFileStorageBase(ABC):
+    """Low-level service responsible for storing and retrieving image files."""
+
+    @abstractmethod
+    def get(self, model_key: str) -> PILImageType:
+        """Retrieves a model image as PIL Image."""
+        pass
+
+    @abstractmethod
+    def get_path(self, model_key: str) -> Path:
+        """Gets the internal path to a model image."""
+        pass
+
+    @abstractmethod
+    def get_url(self, model_key: str) -> str | None:
+        """Gets the URL to fetch a model image."""
+        pass
+
+    @abstractmethod
+    def save(self, image: PILImageType, model_key: str) -> None:
+        """Saves a model image."""
+        pass
+
+    @abstractmethod
+    def delete(self, model_key: str) -> None:
+        """Deletes a model image."""
+        pass

invokeai/app/services/model_images/model_images_common.py

@@ -0,0 +1,20 @@
+# TODO: Should these excpetions subclass existing python exceptions?
+class ModelImageFileNotFoundException(Exception):
+    """Raised when an image file is not found in storage."""
+
+    def __init__(self, message="Model image file not found"):
+        super().__init__(message)
+
+
+class ModelImageFileSaveException(Exception):
+    """Raised when an image cannot be saved."""
+
+    def __init__(self, message="Model image file not saved"):
+        super().__init__(message)
+
+
+class ModelImageFileDeleteException(Exception):
+    """Raised when an image cannot be deleted."""
+
+    def __init__(self, message="Model image file not deleted"):
+        super().__init__(message)

invokeai/app/services/model_images/model_images_default.py

@@ -0,0 +1,79 @@
+from pathlib import Path
+
+from PIL import Image
+from PIL.Image import Image as PILImageType
+from send2trash import send2trash
+
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.util.thumbnails import make_thumbnail
+
+from .model_images_base import ModelImageFileStorageBase
+from .model_images_common import (
+    ModelImageFileDeleteException,
+    ModelImageFileNotFoundException,
+    ModelImageFileSaveException,
+)
+
+
+class ModelImageFileStorageDisk(ModelImageFileStorageBase):
+    """Stores images on disk"""
+
+    def __init__(self, model_images_folder: Path):
+        self._model_images_folder = model_images_folder
+        self._validate_storage_folders()
+
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+
+    def get(self, model_key: str) -> PILImageType:
+        try:
+            path = self.get_path(model_key)
+
+            if not self._validate_path(path):
+                raise ModelImageFileNotFoundException
+
+            return Image.open(path)
+        except FileNotFoundError as e:
+            raise ModelImageFileNotFoundException from e
+
+    def save(self, image: PILImageType, model_key: str) -> None:
+        try:
+            self._validate_storage_folders()
+            image_path = self._model_images_folder / (model_key + ".webp")
+            thumbnail = make_thumbnail(image, 256)
+            thumbnail.save(image_path, format="webp")
+
+        except Exception as e:
+            raise ModelImageFileSaveException from e
+
+    def get_path(self, model_key: str) -> Path:
+        path = self._model_images_folder / (model_key + ".webp")
+
+        return path
+
+    def get_url(self, model_key: str) -> str | None:
+        path = self.get_path(model_key)
+        if not self._validate_path(path):
+            return
+
+        return self._invoker.services.urls.get_model_image_url(model_key)
+
+    def delete(self, model_key: str) -> None:
+        try:
+            path = self.get_path(model_key)
+
+            if not self._validate_path(path):
+                raise ModelImageFileNotFoundException
+
+            send2trash(path)
+
+        except Exception as e:
+            raise ModelImageFileDeleteException from e
+
+    def _validate_path(self, path: Path) -> bool:
+        """Validates the path given for an image."""
+        return path.exists()
+
+    def _validate_storage_folders(self) -> None:
+        """Checks if the required folders exist and create them if they don't"""
+        self._model_images_folder.mkdir(parents=True, exist_ok=True)

invokeai/app/services/model_install/model_install_base.py

@@ -18,10 +18,9 @@ from invokeai.app.services.events.events_base 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.config import ModelSourceType
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
 
-from ..model_metadata import ModelMetadataStoreBase
-
 
 class InstallStatus(str, Enum):
     """State of an install job running in the background."""
@@ -151,6 +150,13 @@ ModelSource = Annotated[
     Union[LocalModelSource, HFModelSource, CivitaiModelSource, URLModelSource], Field(discriminator="type")
 ]
 
+MODEL_SOURCE_TO_TYPE_MAP = {
+    URLModelSource: ModelSourceType.Url,
+    HFModelSource: ModelSourceType.HFRepoID,
+    CivitaiModelSource: ModelSourceType.CivitAI,
+    LocalModelSource: ModelSourceType.Path,
+}
+
 
 class ModelInstallJob(BaseModel):
     """Object that tracks the current status of an install request."""
@@ -260,7 +266,6 @@ class ModelInstallServiceBase(ABC):
         app_config: InvokeAIAppConfig,
         record_store: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
-        metadata_store: ModelMetadataStoreBase,
         event_bus: Optional["EventServiceBase"] = None,
     ):
         """
@@ -347,6 +352,7 @@ class ModelInstallServiceBase(ABC):
         source: str,
         config: Optional[Dict[str, Any]] = None,
         access_token: Optional[str] = None,
+        inplace: Optional[bool] = False,
     ) -> ModelInstallJob:
         r"""Install the indicated model using heuristics to interpret user intentions.
 
@@ -392,7 +398,7 @@ class ModelInstallServiceBase(ABC):
          will override corresponding autoassigned probe fields in the
          model's config record. Use it to override
          `name`, `description`, `base_type`, `model_type`, `format`,
-         `prediction_type`, `image_size`, and/or `ztsnr_training`.
+         `prediction_type`, and/or `image_size`.
 
         This will download the model located at `source`,
         probe it, and install it into the models directory.

invokeai/app/services/model_install/model_install_default.py

@@ -4,6 +4,7 @@ import os
 import re
 import threading
 import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from hashlib import sha256
 from pathlib import Path
 from queue import Empty, Queue
@@ -20,12 +21,15 @@ 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.model_records_base import ModelRecordChanges
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    CheckpointConfigBase,
     InvalidModelConfigException,
     ModelRepoVariant,
+    ModelSourceType,
     ModelType,
 )
 from invokeai.backend.model_manager.metadata import (
@@ -35,12 +39,14 @@ from invokeai.backend.model_manager.metadata import (
     ModelMetadataWithFiles,
     RemoteModelFile,
 )
+from invokeai.backend.model_manager.metadata.metadata_base import CivitaiMetadata, HuggingFaceMetadata
 from invokeai.backend.model_manager.probe import ModelProbe
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util import Chdir, InvokeAILogger
 from invokeai.backend.util.devices import choose_precision, choose_torch_device
 
 from .model_install_base import (
+    MODEL_SOURCE_TO_TYPE_MAP,
     CivitaiModelSource,
     HFModelSource,
     InstallStatus,
@@ -90,7 +96,6 @@ class ModelInstallService(ModelInstallServiceBase):
         self._running = False
         self._session = session
         self._next_job_id = 0
-        self._metadata_store = record_store.metadata_store  # for convenience
 
     @property
     def app_config(self) -> InvokeAIAppConfig:  # noqa D102
@@ -139,6 +144,7 @@ class ModelInstallService(ModelInstallServiceBase):
         config = config or {}
         if not config.get("source"):
             config["source"] = model_path.resolve().as_posix()
+        config["source_type"] = ModelSourceType.Path
         return self._register(model_path, config)
 
     def install_path(
@@ -148,11 +154,11 @@ class ModelInstallService(ModelInstallServiceBase):
     ) -> str:  # noqa D102
         model_path = Path(model_path)
         config = config or {}
-        if not config.get("source"):
-            config["source"] = model_path.resolve().as_posix()
-        config["key"] = config.get("key", uuid_string())
 
-        info: AnyModelConfig = self._probe_model(Path(model_path), config)
+        if self._app_config.skip_model_hash:
+            config["hash"] = uuid_string()
+
+        info: AnyModelConfig = ModelProbe.probe(Path(model_path), config)
 
         if preferred_name := config.get("name"):
             preferred_name = Path(preferred_name).with_suffix(model_path.suffix)
@@ -178,7 +184,7 @@ class ModelInstallService(ModelInstallServiceBase):
         source: str,
         config: Optional[Dict[str, Any]] = None,
         access_token: Optional[str] = None,
-        inplace: bool = False,
+        inplace: Optional[bool] = False,
     ) -> ModelInstallJob:
         variants = "|".join(ModelRepoVariant.__members__.values())
         hf_repoid_re = f"^([^/:]+/[^/:]+)(?::({variants})?(?::/?([^:]+))?)?$"
@@ -275,12 +281,18 @@ class ModelInstallService(ModelInstallServiceBase):
         self._scan_models_directory()
         if autoimport := self._app_config.autoimport_dir:
             self._logger.info("Scanning autoimport directory for new models")
-            installed = self.scan_directory(self._app_config.root_path / autoimport)
+            installed: List[str] = []
+            # Use ThreadPoolExecutor to scan dirs in parallel
+            with ThreadPoolExecutor() as executor:
+                future_models = [executor.submit(self.scan_directory, self._app_config.root_path / autoimport / cur_model_type.value) for cur_model_type in ModelType]
+                [installed.extend(models.result()) for models in as_completed(future_models)]
             self._logger.info(f"{len(installed)} new models registered")
         self._logger.info("Model installer (re)initialized")
 
     def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]:  # noqa D102
         self._cached_model_paths = {Path(x.path).absolute() for x in self.record_store.all_models()}
+        if len([entry for entry in os.scandir(scan_dir) if not entry.name.startswith(".")]) == 0:
+            return []
         callback = self._scan_install if install else self._scan_register
         search = ModelSearch(on_model_found=callback, config=self._app_config)
         self._models_installed.clear()
@@ -374,15 +386,18 @@ class ModelInstallService(ModelInstallServiceBase):
                     job.bytes = job.total_bytes
                     self._signal_job_running(job)
                     job.config_in["source"] = str(job.source)
+                    job.config_in["source_type"] = MODEL_SOURCE_TO_TYPE_MAP[job.source.__class__]
+                    # enter the metadata, if there is any
+                    if isinstance(job.source_metadata, (CivitaiMetadata, HuggingFaceMetadata)):
+                        job.config_in["source_api_response"] = job.source_metadata.api_response
+                    if isinstance(job.source_metadata, CivitaiMetadata) and job.source_metadata.trigger_phrases:
+                        job.config_in["trigger_phrases"] = job.source_metadata.trigger_phrases
+
                     if job.inplace:
                         key = self.register_path(job.local_path, job.config_in)
                     else:
                         key = self.install_path(job.local_path, job.config_in)
                     job.config_out = self.record_store.get_model(key)
-
-                    # enter the metadata, if there is any
-                    if job.source_metadata:
-                        self._metadata_store.add_metadata(key, job.source_metadata)
                     self._signal_job_completed(job)
 
             except InvalidModelConfigException as excp:
@@ -440,10 +455,10 @@ class ModelInstallService(ModelInstallServiceBase):
                 self.unregister(key)
 
             self._logger.info(f"Scanning {self._app_config.models_path} for new and orphaned models")
-            for cur_base_model in BaseModelType:
-                for cur_model_type in ModelType:
-                    models_dir = Path(cur_base_model.value, cur_model_type.value)
-                    installed.update(self.scan_directory(models_dir))
+            # Use ThreadPoolExecutor to scan dirs in parallel
+            with ThreadPoolExecutor() as executor:
+                future_models = [executor.submit(self.scan_directory, Path(cur_base_model.value, cur_model_type.value)) for cur_base_model in BaseModelType for cur_model_type in ModelType]
+                [installed.update(models.result()) for models in as_completed(future_models)]
             self._logger.info(f"{len(installed)} new models registered; {len(defunct_models)} unregistered")
 
     def _sync_model_path(self, key: str) -> AnyModelConfig:
@@ -468,7 +483,7 @@ class ModelInstallService(ModelInstallServiceBase):
         self._logger.info(f"Moving {model.name} to {new_path}.")
         new_path = self._move_model(old_path, new_path)
         model.path = new_path.relative_to(models_dir).as_posix()
-        self.record_store.update_model(key, model)
+        self.record_store.update_model(key, ModelRecordChanges(path=model.path))
         return model
 
     def _scan_register(self, model: Path) -> bool:
@@ -520,24 +535,15 @@ class ModelInstallService(ModelInstallServiceBase):
         move(old_path, new_path)
         return new_path
 
-    def _probe_model(self, model_path: Path, config: Optional[Dict[str, Any]] = None) -> AnyModelConfig:
-        info: AnyModelConfig = ModelProbe.probe(Path(model_path))
-        if config:  # used to override probe fields
-            for key, value in config.items():
-                setattr(info, key, value)
-        return info
-
     def _register(
         self, model_path: Path, config: Optional[Dict[str, Any]] = None, info: Optional[AnyModelConfig] = None
     ) -> str:
-        # Note that we may be passed a pre-populated AnyModelConfig object,
-        # in which case the key field should have been populated by the caller (e.g. in `install_path`).
-        config["key"] = config.get("key", uuid_string())
-        info = info or ModelProbe.probe(model_path, config)
-        override_key: Optional[str] = config.get("key") if config else None
+        config = config or {}
 
-        assert info.original_hash  # always assigned by probe()
-        info.key = override_key or info.original_hash
+        if self._app_config.skip_model_hash:
+            config["hash"] = uuid_string()
+
+        info = info or ModelProbe.probe(model_path, config)
 
         model_path = model_path.absolute()
         if model_path.is_relative_to(self.app_config.models_path):
@@ -546,11 +552,11 @@ class ModelInstallService(ModelInstallServiceBase):
         info.path = model_path.as_posix()
 
         # add 'main' specific fields
-        if hasattr(info, "config"):
+        if isinstance(info, CheckpointConfigBase):
             # make config relative to our root
-            legacy_conf = (self.app_config.root_dir / self.app_config.legacy_conf_dir / info.config).resolve()
-            info.config = legacy_conf.relative_to(self.app_config.root_dir).as_posix()
-        self.record_store.add_model(info.key, info)
+            legacy_conf = (self.app_config.root_dir / self.app_config.legacy_conf_dir / info.config_path).resolve()
+            info.config_path = legacy_conf.relative_to(self.app_config.root_dir).as_posix()
+        self.record_store.add_model(info)
         return info.key
 
     def _next_id(self) -> int:
@@ -571,13 +577,15 @@ class ModelInstallService(ModelInstallServiceBase):
             source=source,
             config_in=config or {},
             local_path=Path(source.path),
-            inplace=source.inplace,
+            inplace=source.inplace or False,
         )
 
     def _import_from_civitai(self, source: CivitaiModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
         if not source.access_token:
             self._logger.info("No Civitai access token provided; some models may not be downloadable.")
-        metadata = CivitaiMetadataFetch(self._session).from_id(str(source.version_id))
+        metadata = CivitaiMetadataFetch(self._session, self.app_config.get_config().civitai_api_key).from_id(
+            str(source.version_id)
+        )
         assert isinstance(metadata, ModelMetadataWithFiles)
         remote_files = metadata.download_urls(session=self._session)
         return self._import_remote_model(source=source, config=config, metadata=metadata, remote_files=remote_files)
@@ -605,15 +613,17 @@ class ModelInstallService(ModelInstallServiceBase):
 
     def _import_from_url(self, source: URLModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
         # URLs from Civitai or HuggingFace will be handled specially
-        url_patterns = {
-            r"^https?://civitai.com/": CivitaiMetadataFetch,
-            r"^https?://huggingface.co/[^/]+/[^/]+$": HuggingFaceMetadataFetch,
-        }
         metadata = None
-        for pattern, fetcher in url_patterns.items():
-            if re.match(pattern, str(source.url), re.IGNORECASE):
-                metadata = fetcher(self._session).from_url(source.url)
-                break
+        fetcher = None
+        try:
+            fetcher = self.get_fetcher_from_url(str(source.url))
+        except ValueError:
+            pass
+        kwargs: dict[str, Any] = {"session": self._session}
+        if fetcher is CivitaiMetadataFetch:
+            kwargs["api_key"] = self._app_config.get_config().civitai_api_key
+        if fetcher is not None:
+            metadata = fetcher(**kwargs).from_url(source.url)
         self._logger.debug(f"metadata={metadata}")
         if metadata and isinstance(metadata, ModelMetadataWithFiles):
             remote_files = metadata.download_urls(session=self._session)
@@ -628,7 +638,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
     def _import_remote_model(
         self,
-        source: ModelSource,
+        source: HFModelSource | CivitaiModelSource | URLModelSource,
         remote_files: List[RemoteModelFile],
         metadata: Optional[AnyModelRepoMetadata],
         config: Optional[Dict[str, Any]],
@@ -656,7 +666,7 @@ class ModelInstallService(ModelInstallServiceBase):
         # 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:
+        if isinstance(source, HFModelSource) and source.subfolder:
             root = Path(remote_files[0].path.parts[0])
             subfolder = root / source.subfolder
         else:
@@ -843,3 +853,11 @@ class ModelInstallService(ModelInstallServiceBase):
         self._logger.info(f"{job.source}: model installation was cancelled")
         if self._event_bus:
             self._event_bus.emit_model_install_cancelled(str(job.source))
+
+    @staticmethod
+    def get_fetcher_from_url(url: str):
+        if re.match(r"^https?://civitai.com/", url.lower()):
+            return CivitaiMetadataFetch
+        elif re.match(r"^https?://huggingface.co/[^/]+/[^/]+$", url.lower()):
+            return HuggingFaceMetadataFetch
+        raise ValueError(f"Unsupported model source: '{url}'")

invokeai/app/services/model_manager/model_manager_base.py

@@ -1,15 +1,11 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 
 from abc import ABC, abstractmethod
-from typing import Optional
 
 import torch
 from typing_extensions import Self
 
 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 ..config import InvokeAIAppConfig
 from ..download import DownloadQueueServiceBase
@@ -70,32 +66,3 @@ class ModelManagerServiceBase(ABC):
     @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(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        pass

invokeai/app/services/model_manager/model_manager_default.py

@@ -1,14 +1,10 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 """Implementation of ModelManagerServiceBase."""
 
-from typing import Optional
-
 import torch
 from typing_extensions import Self
 
 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.devices import choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
@@ -18,7 +14,7 @@ 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_records import ModelRecordServiceBase
 from .model_manager_base import ModelManagerServiceBase
 
 
@@ -64,56 +60,6 @@ class ModelManagerService(ModelManagerServiceBase):
             if hasattr(service, "stop"):
                 service.stop(invoker)
 
-    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)
-
-    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)
-
-    def load_model_by_attr(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        """
-        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
-        """
-        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.")
-        else:
-            return self.load.load_model(configs[0], submodel, context_data)
-
     @classmethod
     def build_model_manager(
         cls,

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

@@ -1,81 +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, Optional, Set, Tuple
-
-from pydantic import Field
-
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
-from invokeai.backend.model_manager.metadata.metadata_base import ModelDefaultSettings
-
-
-class ModelMetadataChanges(BaseModelExcludeNull, extra="allow"):
-    """A set of changes to apply to model metadata.
-    Only limited changes are valid:
-      - `default_settings`: the user-configured default settings for this model
-    """
-
-    default_settings: Optional[ModelDefaultSettings] = Field(
-        default=None, description="The user-configured default settings for this model"
-    )
-    """The user-configured default settings for this model"""
-
-
-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

@@ -1,223 +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: Optional[Set[str]]) -> None:
-        """Update tags for the model referenced by model_key."""
-        if tags:
-            # 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

@@ -6,20 +6,19 @@ Abstract base class for storing and retrieving model configuration records.
 from abc import ABC, abstractmethod
 from enum import Enum
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple, Union
+from typing import List, Optional, Set, Union
 
 from pydantic import BaseModel, Field
 
 from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 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 ModelDefaultSettings, ModelVariantType, SchedulerPredictionType
 
 
 class DuplicateModelException(Exception):
@@ -60,11 +59,34 @@ class ModelSummary(BaseModel):
     tags: Set[str] = Field(description="tags associated with model")
 
 
+class ModelRecordChanges(BaseModelExcludeNull):
+    """A set of changes to apply to a model."""
+
+    # Changes applicable to all models
+    name: Optional[str] = Field(description="Name of the model.", default=None)
+    path: Optional[str] = Field(description="Path to the model.", default=None)
+    description: Optional[str] = Field(description="Model description", default=None)
+    base: Optional[BaseModelType] = Field(description="The base model.", default=None)
+    trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
+    default_settings: Optional[ModelDefaultSettings] = Field(
+        description="Default settings for this model", default=None
+    )
+
+    # Checkpoint-specific changes
+    # TODO(MM2): Should we expose these? Feels footgun-y...
+    variant: Optional[ModelVariantType] = Field(description="The variant of the model.", default=None)
+    prediction_type: Optional[SchedulerPredictionType] = Field(
+        description="The prediction type of the model.", default=None
+    )
+    upcast_attention: Optional[bool] = Field(description="Whether to upcast attention.", default=None)
+    config_path: Optional[str] = Field(description="Path to config file for model", default=None)
+
+
 class ModelRecordServiceBase(ABC):
     """Abstract base class for storage and retrieval of model configs."""
 
     @abstractmethod
-    def add_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
+    def add_model(self, config: AnyModelConfig) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -88,13 +110,12 @@ class ModelRecordServiceBase(ABC):
         pass
 
     @abstractmethod
-    def update_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
+    def update_model(self, key: str, changes: ModelRecordChanges) -> AnyModelConfig:
         """
         Update the model, returning the updated version.
 
-        :param key: Unique key for the model to be updated
-        :param config: Model configuration record. Either a dict with the
-         required fields, or a ModelConfigBase instance.
+        :param key: Unique key for the model to be updated.
+        :param changes: A set of changes to apply to this model. Changes are validated before being written.
         """
         pass
 
@@ -109,40 +130,17 @@ class ModelRecordServiceBase(ABC):
         """
         pass
 
-    @property
     @abstractmethod
-    def metadata_store(self) -> ModelMetadataStoreBase:
-        """Return a ModelMetadataStore initialized on the same database."""
-        pass
-
-    @abstractmethod
-    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
+    def get_model_by_hash(self, hash: str) -> AnyModelConfig:
         """
-        Retrieve metadata (if any) from when model was downloaded from a repo.
+        Retrieve the configuration for the indicated model.
 
-        :param key: Model key
+        :param hash: Hash of model config to be fetched.
+
+        Exceptions: UnknownModelException
         """
         pass
 
-    @abstractmethod
-    def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:
-        """List metadata for all models that have it."""
-        pass
-
-    @abstractmethod
-    def search_by_metadata_tag(self, tags: Set[str]) -> List[AnyModelConfig]:
-        """
-        Search model metadata for ones with all listed tags and return their corresponding configs.
-
-        :param tags: Set of tags to search for. All tags must be present.
-        """
-        pass
-
-    @abstractmethod
-    def list_tags(self) -> Set[str]:
-        """Return a unique set of all the model tags in the metadata database."""
-        pass
-
     @abstractmethod
     def list_models(
         self, page: int = 0, per_page: int = 10, order_by: ModelRecordOrderBy = ModelRecordOrderBy.Default
@@ -217,21 +215,3 @@ class ModelRecordServiceBase(ABC):
                 f"More than one model matched the search criteria: base_model='{base_model}', model_type='{model_type}', model_name='{model_name}'."
             )
         return model_configs[0]
-
-    def rename_model(
-        self,
-        key: str,
-        new_name: str,
-    ) -> AnyModelConfig:
-        """
-        Rename the indicated model. Just a special case of update_model().
-
-        In some implementations, renaming the model may involve changing where
-        it is stored on the filesystem. So this is broken out.
-
-        :param key: Model key
-        :param new_name: New name for model
-        """
-        config = self.get_model(key)
-        config.name = new_name
-        return self.update_model(key, config)

invokeai/app/services/model_records/model_records_sql.py

@@ -43,7 +43,7 @@ import json
 import sqlite3
 from math import ceil
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple, Union
+from typing import List, Optional, Union
 
 from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.backend.model_manager.config import (
@@ -53,12 +53,11 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, UnknownMetadataException
 
-from ..model_metadata import ModelMetadataStoreBase, ModelMetadataStoreSQL
 from ..shared.sqlite.sqlite_database import SqliteDatabase
 from .model_records_base import (
     DuplicateModelException,
+    ModelRecordChanges,
     ModelRecordOrderBy,
     ModelRecordServiceBase,
     ModelSummary,
@@ -69,7 +68,7 @@ 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.
 
@@ -78,14 +77,13 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         super().__init__()
         self._db = db
         self._cursor = db.conn.cursor()
-        self._metadata_store = metadata_store
 
     @property
     def db(self) -> SqliteDatabase:
         """Return the underlying database."""
         return self._db
 
-    def add_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
+    def add_model(self, config: AnyModelConfig) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -95,23 +93,19 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
         Can raise DuplicateModelException and InvalidModelConfigException exceptions.
         """
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect.
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
         with self._db.lock:
             try:
                 self._cursor.execute(
                     """--sql
-                    INSERT INTO model_config (
+                    INSERT INTO models (
                        id,
-                       original_hash,
                        config
                       )
-                    VALUES (?,?,?);
+                    VALUES (?,?);
                     """,
                     (
-                        key,
-                        record.original_hash,
-                        json_serialized,
+                        config.key,
+                        config.model_dump_json(),
                     ),
                 )
                 self._db.conn.commit()
@@ -119,12 +113,12 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             except sqlite3.IntegrityError as e:
                 self._db.conn.rollback()
                 if "UNIQUE constraint failed" in str(e):
-                    if "model_config.path" in str(e):
-                        msg = f"A model with path '{record.path}' is already installed"
-                    elif "model_config.name" in str(e):
-                        msg = f"A model with name='{record.name}', type='{record.type}', base='{record.base}' is already installed"
+                    if "models.path" in str(e):
+                        msg = f"A model with path '{config.path}' is already installed"
+                    elif "models.name" in str(e):
+                        msg = f"A model with name='{config.name}', type='{config.type}', base='{config.base}' is already installed"
                     else:
-                        msg = f"A model with key '{key}' is already installed"
+                        msg = f"A model with key '{config.key}' is already installed"
                     raise DuplicateModelException(msg) from e
                 else:
                     raise e
@@ -132,7 +126,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 self._db.conn.rollback()
                 raise e
 
-        return self.get_model(key)
+        return self.get_model(config.key)
 
     def del_model(self, key: str) -> None:
         """
@@ -146,7 +140,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             try:
                 self._cursor.execute(
                     """--sql
-                    DELETE FROM model_config
+                    DELETE FROM models
                     WHERE id=?;
                     """,
                     (key,),
@@ -158,21 +152,20 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 self._db.conn.rollback()
                 raise e
 
-    def update_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
-        """
-        Update the model, returning the updated version.
+    def update_model(self, key: str, changes: ModelRecordChanges) -> AnyModelConfig:
+        record = self.get_model(key)
+
+        # Model configs use pydantic's `validate_assignment`, so each change is validated by pydantic.
+        for field_name in changes.model_fields_set:
+            setattr(record, field_name, getattr(changes, field_name))
+
+        json_serialized = record.model_dump_json()
 
-        :param key: Unique key for the model to be updated
-        :param config: Model configuration record. Either a dict with the
-         required fields, or a ModelConfigBase instance.
-        """
-        record = ModelConfigFactory.make_config(config, key=key)  # ensure it is a valid config obect
-        json_serialized = record.model_dump_json()  # and turn it into a json string.
         with self._db.lock:
             try:
                 self._cursor.execute(
                     """--sql
-                    UPDATE model_config
+                    UPDATE models
                     SET
                         config=?
                     WHERE id=?;
@@ -199,7 +192,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 """--sql
-                SELECT config, strftime('%s',updated_at) FROM model_config
+                SELECT config, strftime('%s',updated_at) FROM models
                 WHERE id=?;
                 """,
                 (key,),
@@ -210,6 +203,21 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
         return model
 
+    def get_model_by_hash(self, hash: str) -> AnyModelConfig:
+        with self._db.lock:
+            self._cursor.execute(
+                """--sql
+                SELECT config, strftime('%s',updated_at) FROM models
+                WHERE hash=?;
+                """,
+                (hash,),
+            )
+            rows = self._cursor.fetchone()
+            if not rows:
+                raise UnknownModelException("model not found")
+            model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
+        return model
+
     def exists(self, key: str) -> bool:
         """
         Return True if a model with the indicated key exists in the databse.
@@ -220,7 +228,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 """--sql
-                select count(*) FROM model_config
+                select count(*) FROM models
                 WHERE id=?;
                 """,
                 (key,),
@@ -246,9 +254,8 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         If none of the optional filters are passed, will return all
         models in the database.
         """
-        results = []
-        where_clause = []
-        bindings = []
+        where_clause: list[str] = []
+        bindings: list[str] = []
         if model_name:
             where_clause.append("name=?")
             bindings.append(model_name)
@@ -265,14 +272,13 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 f"""--sql
-                select config, strftime('%s',updated_at) FROM model_config
+                SELECT config, strftime('%s',updated_at) FROM models
                 {where};
                 """,
                 tuple(bindings),
             )
-            results = [
-                ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in self._cursor.fetchall()
-            ]
+            result = self._cursor.fetchall()
+            results = [ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in result]
         return results
 
     def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
@@ -281,7 +287,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 """--sql
-                SELECT config, strftime('%s',updated_at) FROM model_config
+                SELECT config, strftime('%s',updated_at) FROM models
                 WHERE path=?;
                 """,
                 (str(path),),
@@ -292,13 +298,13 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         return results
 
     def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
-        """Return models with the indicated original_hash."""
+        """Return models with the indicated hash."""
         results = []
         with self._db.lock:
             self._cursor.execute(
                 """--sql
-                SELECT config, strftime('%s',updated_at) FROM model_config
-                WHERE original_hash=?;
+                SELECT config, strftime('%s',updated_at) FROM models
+                WHERE hash=?;
                 """,
                 (hash,),
             )
@@ -307,83 +313,35 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             ]
         return results
 
-    @property
-    def metadata_store(self) -> ModelMetadataStoreBase:
-        """Return a ModelMetadataStore initialized on the same database."""
-        return self._metadata_store
-
-    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
-        """
-        Retrieve metadata (if any) from when model was downloaded from a repo.
-
-        :param key: Model key
-        """
-        store = self.metadata_store
-        try:
-            metadata = store.get_metadata(key)
-            return metadata
-        except UnknownMetadataException:
-            return None
-
-    def search_by_metadata_tag(self, tags: Set[str]) -> List[AnyModelConfig]:
-        """
-        Search model metadata for ones with all listed tags and return their corresponding configs.
-
-        :param tags: Set of tags to search for. All tags must be present.
-        """
-        store = ModelMetadataStoreSQL(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)
-        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)
-        return store.list_all_metadata()
-
     def list_models(
         self, page: int = 0, per_page: int = 10, order_by: ModelRecordOrderBy = ModelRecordOrderBy.Default
     ) -> PaginatedResults[ModelSummary]:
         """Return a paginated summary listing of each model in the database."""
+        assert isinstance(order_by, ModelRecordOrderBy)
         ordering = {
-            ModelRecordOrderBy.Default: "a.type, a.base, a.format, a.name",
-            ModelRecordOrderBy.Type: "a.type",
-            ModelRecordOrderBy.Base: "a.base",
-            ModelRecordOrderBy.Name: "a.name",
-            ModelRecordOrderBy.Format: "a.format",
+            ModelRecordOrderBy.Default: "type, base, format, name",
+            ModelRecordOrderBy.Type: "type",
+            ModelRecordOrderBy.Base: "base",
+            ModelRecordOrderBy.Name: "name",
+            ModelRecordOrderBy.Format: "format",
         }
 
-        def _fixup(summary: Dict[str, str]) -> Dict[str, Union[str, int, Set[str]]]:
-            """Fix up results so that there are no null values."""
-            result: Dict[str, Union[str, int, Set[str]]] = {}
-            for key, item in summary.items():
-                result[key] = item or ""
-            result["tags"] = set(json.loads(summary["tags"] or "[]"))
-            return result
-
         # Lock so that the database isn't updated while we're doing the two queries.
         with self._db.lock:
             # query1: get the total number of model configs
             self._cursor.execute(
                 """--sql
-                select count(*) from model_config;
+                select count(*) from models;
                 """,
                 (),
             )
             total = int(self._cursor.fetchone()[0])
 
-            # query2: fetch key fields from the join of model_config and model_metadata
+            # query2: fetch key fields
             self._cursor.execute(
                 f"""--sql
-                SELECT a.id as key, a.type, a.base, a.format, a.name,
-                       json_extract(a.config, '$.description') as description,
-                       json_extract(b.metadata, '$.tags') as tags
-                FROM model_config AS a
-                LEFT JOIN model_metadata AS b on a.id=b.id
+                SELECT config
+                FROM models
                 ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason
                 LIMIT ?
                 OFFSET ?;
@@ -394,7 +352,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 ),
             )
             rows = self._cursor.fetchall()
-            items = [ModelSummary.model_validate(_fixup(dict(x))) for x in rows]
+            items = [ModelSummary.model_validate(dict(x)) for x in rows]
             return PaginatedResults(
                 page=page, pages=ceil(total / per_page), per_page=per_page, total=total, items=items
             )

invokeai/app/services/shared/invocation_context.py

@@ -1,7 +1,7 @@
 import threading
 from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Optional, Union
 
 from PIL.Image import Image
 from torch import Tensor
@@ -13,15 +13,16 @@ 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.model_records.model_records_base import UnknownModelException
 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
+    from invokeai.app.invocations.model import ModelField
     from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
 
 """
@@ -299,22 +300,25 @@ class ConditioningInterface(InvocationContextInterface):
 
 
 class ModelsInterface(InvocationContextInterface):
-    def exists(self, key: str) -> bool:
+    def exists(self, identifier: Union[str, "ModelField"]) -> bool:
         """Checks if a model exists.
 
         Args:
-            key: The key of the model.
+            identifier: The key or ModelField representing the model.
 
         Returns:
             True if the model exists, False if not.
         """
-        return self._services.model_manager.store.exists(key)
+        if isinstance(identifier, str):
+            return self._services.model_manager.store.exists(identifier)
 
-    def load(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+        return self._services.model_manager.store.exists(identifier.key)
+
+    def load(self, identifier: Union[str, "ModelField"], submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """Loads a model.
 
         Args:
-            key: The key of the model.
+            identifier: The key or ModelField representing the model.
             submodel_type: The submodel of the model to get.
 
         Returns:
@@ -324,9 +328,13 @@ class ModelsInterface(InvocationContextInterface):
         # 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._data
-        )
+        if isinstance(identifier, str):
+            model = self._services.model_manager.store.get_model(identifier)
+            return self._services.model_manager.load.load_model(model, submodel_type, self._data)
+        else:
+            _submodel_type = submodel_type or identifier.submodel_type
+            model = self._services.model_manager.store.get_model(identifier.key)
+            return self._services.model_manager.load.load_model(model, _submodel_type, self._data)
 
     def load_by_attrs(
         self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
@@ -343,35 +351,29 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             An object representing the loaded model.
         """
-        return self._services.model_manager.load_model_by_attr(
-            model_name=name,
-            base_model=base,
-            model_type=type,
-            submodel=submodel_type,
-            context_data=self._data,
-        )
 
-    def get_config(self, key: str) -> AnyModelConfig:
+        configs = self._services.model_manager.store.search_by_attr(model_name=name, base_model=base, model_type=type)
+        if len(configs) == 0:
+            raise UnknownModelException(f"No model found with name {name}, base {base}, and type {type}")
+
+        if len(configs) > 1:
+            raise ValueError(f"More than one model found with name {name}, base {base}, and type {type}")
+
+        return self._services.model_manager.load.load_model(configs[0], submodel_type, self._data)
+
+    def get_config(self, identifier: Union[str, "ModelField"]) -> AnyModelConfig:
         """Gets a model's config.
 
         Args:
-            key: The key of the model.
+            identifier: The key or ModelField representing the model.
 
         Returns:
             The model's config.
         """
-        return self._services.model_manager.store.get_model(key=key)
+        if isinstance(identifier, str):
+            return self._services.model_manager.store.get_model(identifier)
 
-    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
-        """Gets a model's metadata, if it has any.
-
-        Args:
-            key: The key of the model.
-
-        Returns:
-            The model's metadata, if it has any.
-        """
-        return self._services.model_manager.store.get_metadata(key=key)
+        return self._services.model_manager.store.get_model(identifier.key)
 
     def search_by_path(self, path: Path) -> list[AnyModelConfig]:
         """Searches for models by path.

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

@@ -9,6 +9,7 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_3 import
 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.migrations.migration_7 import build_migration_7
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -35,6 +36,7 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_4())
     migrator.register_migration(build_migration_5())
     migrator.register_migration(build_migration_6())
+    migrator.register_migration(build_migration_7())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_7.py

@@ -0,0 +1,88 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration7Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._create_models_table(cursor)
+        self._drop_old_models_tables(cursor)
+
+    def _drop_old_models_tables(self, cursor: sqlite3.Cursor) -> None:
+        """Drops the old model_records, model_metadata, model_tags and tags tables."""
+
+        tables = ["model_records", "model_metadata", "model_tags", "tags"]
+
+        for table in tables:
+            cursor.execute(f"DROP TABLE IF EXISTS {table};")
+
+    def _create_models_table(self, cursor: sqlite3.Cursor) -> None:
+        """Creates the v4.0.0 models table."""
+
+        tables = [
+            """--sql
+            CREATE TABLE IF NOT EXISTS models (
+                id TEXT NOT NULL PRIMARY KEY,
+                hash TEXT GENERATED ALWAYS as (json_extract(config, '$.hash')) VIRTUAL NOT NULL,
+                base TEXT GENERATED ALWAYS as (json_extract(config, '$.base')) VIRTUAL NOT NULL,
+                type TEXT GENERATED ALWAYS as (json_extract(config, '$.type')) VIRTUAL NOT NULL,
+                path TEXT GENERATED ALWAYS as (json_extract(config, '$.path')) VIRTUAL NOT NULL,
+                format TEXT GENERATED ALWAYS as (json_extract(config, '$.format')) VIRTUAL NOT NULL,
+                name TEXT GENERATED ALWAYS as (json_extract(config, '$.name')) VIRTUAL NOT NULL,
+                description TEXT GENERATED ALWAYS as (json_extract(config, '$.description')) VIRTUAL,
+                source TEXT GENERATED ALWAYS as (json_extract(config, '$.source')) VIRTUAL NOT NULL,
+                source_type TEXT GENERATED ALWAYS as (json_extract(config, '$.source_type')) VIRTUAL NOT NULL,
+                source_api_response TEXT GENERATED ALWAYS as (json_extract(config, '$.source_api_response')) VIRTUAL,
+                trigger_phrases TEXT GENERATED ALWAYS as (json_extract(config, '$.trigger_phrases')) VIRTUAL,
+                -- Serialized JSON representation of the whole config object, which will contain additional fields from subclasses
+                config TEXT NOT NULL,
+                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- Updated via trigger
+                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                -- unique constraint on combo of name, base and type
+                UNIQUE(name, base, type)
+            );
+            """
+        ]
+
+        # Add trigger for `updated_at`.
+        triggers = [
+            """--sql
+            CREATE TRIGGER IF NOT EXISTS models_updated_at
+            AFTER UPDATE
+            ON models FOR EACH ROW
+            BEGIN
+                UPDATE models SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE id = old.id;
+            END;
+            """
+        ]
+
+        # Add indexes for searchable fields
+        indices = [
+            "CREATE INDEX IF NOT EXISTS base_index ON models(base);",
+            "CREATE INDEX IF NOT EXISTS type_index ON models(type);",
+            "CREATE INDEX IF NOT EXISTS name_index ON models(name);",
+            "CREATE UNIQUE INDEX IF NOT EXISTS path_index ON models(path);",
+        ]
+
+        for stmt in tables + indices + triggers:
+            cursor.execute(stmt)
+
+
+def build_migration_7() -> Migration:
+    """
+    Build the migration from database version 6 to 7.
+
+    This migration does the following:
+    - Adds the new models table
+    - Drops the old model_records, model_metadata, model_tags and tags tables.
+    - TODO(MM2): Migrates model names and descriptions from `models.yaml` to the new table (?).
+    """
+    migration_7 = Migration(
+        from_version=6,
+        to_version=7,
+        callback=Migration7Callback(),
+    )
+
+    return migration_7

invokeai/app/services/shared/sqlite_migrator/migrations/util/migrate_yaml_config_1.py

@@ -150,7 +150,7 @@ class MigrateModelYamlToDb1:
                 """,
                 (
                     key,
-                    record.original_hash,
+                    record.hash,
                     json_serialized,
                 ),
             )

invokeai/app/services/urls/urls_base.py

@@ -8,3 +8,8 @@ class UrlServiceBase(ABC):
     def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:
         """Gets the URL for an image or thumbnail."""
         pass
+
+    @abstractmethod
+    def get_model_image_url(self, model_key: str) -> str:
+        """Gets the URL for a model image"""
+        pass

invokeai/app/services/urls/urls_default.py

@@ -4,8 +4,9 @@ from .urls_base import UrlServiceBase
 
 
 class LocalUrlService(UrlServiceBase):
-    def __init__(self, base_url: str = "api/v1"):
+    def __init__(self, base_url: str = "api/v1", base_url_v2: str = "api/v2"):
         self._base_url = base_url
+        self._base_url_v2 = base_url_v2
 
     def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:
         image_basename = os.path.basename(image_name)
@@ -15,3 +16,6 @@ class LocalUrlService(UrlServiceBase):
             return f"{self._base_url}/images/i/{image_basename}/thumbnail"
 
         return f"{self._base_url}/images/i/{image_basename}/full"
+
+    def get_model_image_url(self, model_key: str) -> str:
+        return f"{self._base_url_v2}/models/i/{model_key}/image"

invokeai/app/util/metadata.py

@@ -1,55 +0,0 @@
-import json
-from typing import Optional
-
-from pydantic import ValidationError
-
-from invokeai.app.services.shared.graph import Edge
-
-
-def get_metadata_graph_from_raw_session(session_raw: str) -> Optional[dict]:
-    """
-    Parses raw session string, returning a dict of the graph.
-
-    Only the general graph shape is validated; none of the fields are validated.
-
-    Any `metadata_accumulator` nodes and edges are removed.
-
-    Any validation failure will return None.
-    """
-
-    graph = json.loads(session_raw).get("graph", None)
-
-    # sanity check make sure the graph is at least reasonably shaped
-    if (
-        not isinstance(graph, dict)
-        or "nodes" not in graph
-        or not isinstance(graph["nodes"], dict)
-        or "edges" not in graph
-        or not isinstance(graph["edges"], list)
-    ):
-        # something has gone terribly awry, return an empty dict
-        return None
-
-    try:
-        # delete the `metadata_accumulator` node
-        del graph["nodes"]["metadata_accumulator"]
-    except KeyError:
-        # no accumulator node, all good
-        pass
-
-    # delete any edges to or from it
-    for i, edge in enumerate(graph["edges"]):
-        try:
-            # try to parse the edge
-            Edge(**edge)
-        except ValidationError:
-            # something has gone terribly awry, return an empty dict
-            return None
-
-        if (
-            edge["source"]["node_id"] == "metadata_accumulator"
-            or edge["destination"]["node_id"] == "metadata_accumulator"
-        ):
-            del graph["edges"][i]
-
-    return graph

invokeai/app/util/ti_utils.py

@@ -22,7 +22,7 @@ def generate_ti_list(
     for trigger in extract_ti_triggers_from_prompt(prompt):
         name_or_key = trigger[1:-1]
         try:
-            loaded_model = context.models.load(key=name_or_key)
+            loaded_model = context.models.load(name_or_key)
             model = loaded_model.model
             assert isinstance(model, TextualInversionModelRaw)
             assert loaded_model.config.base == base

invokeai/backend/install/install_helper.py

@@ -19,7 +19,6 @@ from invokeai.app.services.model_install import (
     ModelInstallService,
     ModelInstallServiceBase,
 )
-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 (
@@ -39,7 +38,7 @@ 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
 
 

invokeai/backend/model_manager/config.py

@@ -25,10 +25,13 @@ from enum import Enum
 from typing import Literal, Optional, Type, Union
 
 import torch
-from diffusers import ModelMixin
-from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
+from diffusers.models.modeling_utils import ModelMixin
+from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, TypeAdapter
 from typing_extensions import Annotated, Any, Dict
 
+from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
+from invokeai.app.util.misc import uuid_string
+
 from ..raw_model import RawModel
 
 # ModelMixin is the base class for all diffusers and transformers models
@@ -56,8 +59,8 @@ class ModelType(str, Enum):
 
     ONNX = "onnx"
     Main = "main"
-    Vae = "vae"
-    Lora = "lora"
+    VAE = "vae"
+    LoRA = "lora"
     ControlNet = "controlnet"  # used by model_probe
     TextualInversion = "embedding"
     IPAdapter = "ip_adapter"
@@ -73,9 +76,9 @@ class SubModelType(str, Enum):
     TextEncoder2 = "text_encoder_2"
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
-    Vae = "vae"
-    VaeDecoder = "vae_decoder"
-    VaeEncoder = "vae_encoder"
+    VAE = "vae"
+    VAEDecoder = "vae_decoder"
+    VAEEncoder = "vae_encoder"
     Scheduler = "scheduler"
     SafetyChecker = "safety_checker"
 
@@ -93,8 +96,8 @@ class ModelFormat(str, Enum):
 
     Diffusers = "diffusers"
     Checkpoint = "checkpoint"
-    Lycoris = "lycoris"
-    Onnx = "onnx"
+    LyCORIS = "lycoris"
+    ONNX = "onnx"
     Olive = "olive"
     EmbeddingFile = "embedding_file"
     EmbeddingFolder = "embedding_folder"
@@ -112,128 +115,188 @@ class SchedulerPredictionType(str, Enum):
 class ModelRepoVariant(str, Enum):
     """Various hugging face variants on the diffusers format."""
 
-    DEFAULT = ""  # model files without "fp16" or other qualifier - empty str
+    Default = ""  # model files without "fp16" or other qualifier - empty str
     FP16 = "fp16"
     FP32 = "fp32"
     ONNX = "onnx"
-    OPENVINO = "openvino"
-    FLAX = "flax"
+    OpenVINO = "openvino"
+    Flax = "flax"
+
+
+class ModelSourceType(str, Enum):
+    """Model source type."""
+
+    Path = "path"
+    Url = "url"
+    HFRepoID = "hf_repo_id"
+    CivitAI = "civitai"
+
+
+class ModelDefaultSettings(BaseModel):
+    vae: str | None
+    vae_precision: str | None
+    scheduler: SCHEDULER_NAME_VALUES | None
+    steps: int | None
+    cfg_scale: float | None
+    cfg_rescale_multiplier: float | None
 
 
 class ModelConfigBase(BaseModel):
     """Base class for model configuration information."""
 
-    path: str = Field(description="filesystem path to the model file or directory")
-    name: str = Field(description="model name")
-    base: BaseModelType = Field(description="base model")
-    type: ModelType = Field(description="type of the model")
-    format: ModelFormat = Field(description="model format")
-    key: str = Field(description="unique key for model", default="<NOKEY>")
-    original_hash: Optional[str] = Field(
-        description="original fasthash of model contents", default=None
-    )  # this is assigned at install time and will not change
-    current_hash: Optional[str] = Field(
-        description="current fasthash of model contents", default=None
-    )  # if model is converted or otherwise modified, this will hold updated hash
-    description: Optional[str] = Field(description="human readable description of the model", default=None)
-    source: Optional[str] = Field(description="model original source (path, URL or repo_id)", default=None)
-    last_modified: Optional[float] = Field(description="timestamp for modification time", default_factory=time.time)
+    key: str = Field(description="A unique key for this model.", default_factory=uuid_string)
+    hash: str = Field(description="The hash of the model file(s).")
+    path: str = Field(
+        description="Path to the model on the filesystem. Relative paths are relative to the Invoke root directory."
+    )
+    name: str = Field(description="Name of the model.")
+    base: BaseModelType = Field(description="The base model.")
+    description: Optional[str] = Field(description="Model description", default=None)
+    source: str = Field(description="The original source of the model (path, URL or repo_id).")
+    source_type: ModelSourceType = Field(description="The type of source")
+    source_api_response: Optional[str] = Field(
+        description="The original API response from the source, as stringified JSON.", default=None
+    )
+    trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
+    default_settings: Optional[ModelDefaultSettings] = Field(
+        description="Default settings for this model", default=None
+    )
+    cover_image: Optional[str] = Field(description="Url for image to preview model", default=None)
 
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
-        schema["required"].extend(
-            ["key", "base", "type", "format", "original_hash", "current_hash", "source", "last_modified"]
-        )
+        schema["required"].extend(["key", "type", "format"])
 
-    model_config = ConfigDict(
-        use_enum_values=False,
-        validate_assignment=True,
-        json_schema_extra=json_schema_extra,
-    )
-
-    def update(self, attributes: Dict[str, Any]) -> None:
-        """Update the object with fields in dict."""
-        for key, value in attributes.items():
-            setattr(self, key, value)  # may raise a validation error
+    model_config = ConfigDict(validate_assignment=True, json_schema_extra=json_schema_extra)
 
 
-class _CheckpointConfig(ModelConfigBase):
+class CheckpointConfigBase(ModelConfigBase):
     """Model config for checkpoint-style models."""
 
     format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
-    config: str = Field(description="path to the checkpoint model config file")
+    config_path: str = Field(description="path to the checkpoint model config file")
+    converted_at: Optional[float] = Field(
+        description="When this model was last converted to diffusers", default_factory=time.time
+    )
 
 
-class _DiffusersConfig(ModelConfigBase):
+class DiffusersConfigBase(ModelConfigBase):
     """Model config for diffusers-style models."""
 
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
-    repo_variant: Optional[ModelRepoVariant] = ModelRepoVariant.DEFAULT
+    repo_variant: Optional[ModelRepoVariant] = ModelRepoVariant.Default
 
 
-class LoRAConfig(ModelConfigBase):
+class LoRALyCORISConfig(ModelConfigBase):
     """Model config for LoRA/Lycoris models."""
 
-    type: Literal[ModelType.Lora] = ModelType.Lora
-    format: Literal[ModelFormat.Lycoris, ModelFormat.Diffusers]
+    type: Literal[ModelType.LoRA] = ModelType.LoRA
+    format: Literal[ModelFormat.LyCORIS] = ModelFormat.LyCORIS
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.LoRA.value}.{ModelFormat.LyCORIS.value}")
 
 
-class VaeCheckpointConfig(ModelConfigBase):
+class LoRADiffusersConfig(ModelConfigBase):
+    """Model config for LoRA/Diffusers models."""
+
+    type: Literal[ModelType.LoRA] = ModelType.LoRA
+    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.LoRA.value}.{ModelFormat.Diffusers.value}")
+
+
+class VAECheckpointConfig(CheckpointConfigBase):
     """Model config for standalone VAE models."""
 
-    type: Literal[ModelType.Vae] = ModelType.Vae
+    type: Literal[ModelType.VAE] = ModelType.VAE
     format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.VAE.value}.{ModelFormat.Checkpoint.value}")
 
-class VaeDiffusersConfig(ModelConfigBase):
+
+class VAEDiffusersConfig(ModelConfigBase):
     """Model config for standalone VAE models (diffusers version)."""
 
-    type: Literal[ModelType.Vae] = ModelType.Vae
+    type: Literal[ModelType.VAE] = ModelType.VAE
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.VAE.value}.{ModelFormat.Diffusers.value}")
 
-class ControlNetDiffusersConfig(_DiffusersConfig):
+
+class ControlNetDiffusersConfig(DiffusersConfigBase):
     """Model config for ControlNet models (diffusers version)."""
 
     type: Literal[ModelType.ControlNet] = ModelType.ControlNet
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.ControlNet.value}.{ModelFormat.Diffusers.value}")
 
-class ControlNetCheckpointConfig(_CheckpointConfig):
+
+class ControlNetCheckpointConfig(CheckpointConfigBase):
     """Model config for ControlNet models (diffusers version)."""
 
     type: Literal[ModelType.ControlNet] = ModelType.ControlNet
     format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.ControlNet.value}.{ModelFormat.Checkpoint.value}")
 
-class TextualInversionConfig(ModelConfigBase):
+
+class TextualInversionFileConfig(ModelConfigBase):
     """Model config for textual inversion embeddings."""
 
     type: Literal[ModelType.TextualInversion] = ModelType.TextualInversion
-    format: Literal[ModelFormat.EmbeddingFile, ModelFormat.EmbeddingFolder]
+    format: Literal[ModelFormat.EmbeddingFile] = ModelFormat.EmbeddingFile
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.TextualInversion.value}.{ModelFormat.EmbeddingFile.value}")
 
 
-class _MainConfig(ModelConfigBase):
-    """Model config for main models."""
+class TextualInversionFolderConfig(ModelConfigBase):
+    """Model config for textual inversion embeddings."""
 
-    vae: Optional[str] = Field(default=None)
-    variant: ModelVariantType = ModelVariantType.Normal
-    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
-    upcast_attention: bool = False
-    ztsnr_training: bool = False
+    type: Literal[ModelType.TextualInversion] = ModelType.TextualInversion
+    format: Literal[ModelFormat.EmbeddingFolder] = ModelFormat.EmbeddingFolder
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.TextualInversion.value}.{ModelFormat.EmbeddingFolder.value}")
 
 
-class MainCheckpointConfig(_CheckpointConfig, _MainConfig):
+class MainCheckpointConfig(CheckpointConfigBase):
     """Model config for main checkpoint models."""
 
     type: Literal[ModelType.Main] = ModelType.Main
+    variant: ModelVariantType = ModelVariantType.Normal
+    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
+    upcast_attention: bool = False
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.Main.value}.{ModelFormat.Checkpoint.value}")
 
 
-class MainDiffusersConfig(_DiffusersConfig, _MainConfig):
+class MainDiffusersConfig(DiffusersConfigBase):
     """Model config for main diffusers models."""
 
     type: Literal[ModelType.Main] = ModelType.Main
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.Main.value}.{ModelFormat.Diffusers.value}")
+
 
 class IPAdapterConfig(ModelConfigBase):
     """Model config for IP Adaptor format models."""
@@ -242,65 +305,76 @@ class IPAdapterConfig(ModelConfigBase):
     image_encoder_model_id: str
     format: Literal[ModelFormat.InvokeAI]
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.IPAdapter.value}.{ModelFormat.InvokeAI.value}")
+
 
 class CLIPVisionDiffusersConfig(ModelConfigBase):
-    """Model config for ClipVision."""
+    """Model config for CLIPVision."""
 
     type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision
     format: Literal[ModelFormat.Diffusers]
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.CLIPVision.value}.{ModelFormat.Diffusers.value}")
 
-class T2IConfig(ModelConfigBase):
+
+class T2IAdapterConfig(ModelConfigBase):
     """Model config for T2I."""
 
     type: Literal[ModelType.T2IAdapter] = ModelType.T2IAdapter
     format: Literal[ModelFormat.Diffusers]
 
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.T2IAdapter.value}.{ModelFormat.Diffusers.value}")
 
-_ControlNetConfig = Annotated[
-    Union[ControlNetDiffusersConfig, ControlNetCheckpointConfig],
-    Field(discriminator="format"),
-]
-_VaeConfig = Annotated[Union[VaeDiffusersConfig, VaeCheckpointConfig], Field(discriminator="format")]
-_MainModelConfig = Annotated[Union[MainDiffusersConfig, MainCheckpointConfig], Field(discriminator="format")]
 
-AnyModelConfig = Union[
-    _MainModelConfig,
-    _VaeConfig,
-    _ControlNetConfig,
-    # ModelConfigBase,
-    LoRAConfig,
-    TextualInversionConfig,
-    IPAdapterConfig,
-    CLIPVisionDiffusersConfig,
-    T2IConfig,
+def get_model_discriminator_value(v: Any) -> str:
+    """
+    Computes the discriminator value for a model config.
+    https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions-with-callable-discriminator
+    """
+    format_ = None
+    type_ = None
+    if isinstance(v, dict):
+        format_ = v.get("format")
+        if isinstance(format_, Enum):
+            format_ = format_.value
+        type_ = v.get("type")
+        if isinstance(type_, Enum):
+            type_ = type_.value
+    else:
+        format_ = v.format.value
+        type_ = v.type.value
+    v = f"{type_}.{format_}"
+    return v
+
+
+AnyModelConfig = Annotated[
+    Union[
+        Annotated[MainDiffusersConfig, MainDiffusersConfig.get_tag()],
+        Annotated[MainCheckpointConfig, MainCheckpointConfig.get_tag()],
+        Annotated[VAEDiffusersConfig, VAEDiffusersConfig.get_tag()],
+        Annotated[VAECheckpointConfig, VAECheckpointConfig.get_tag()],
+        Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
+        Annotated[ControlNetCheckpointConfig, ControlNetCheckpointConfig.get_tag()],
+        Annotated[LoRALyCORISConfig, LoRALyCORISConfig.get_tag()],
+        Annotated[LoRADiffusersConfig, LoRADiffusersConfig.get_tag()],
+        Annotated[TextualInversionFileConfig, TextualInversionFileConfig.get_tag()],
+        Annotated[TextualInversionFolderConfig, TextualInversionFolderConfig.get_tag()],
+        Annotated[IPAdapterConfig, IPAdapterConfig.get_tag()],
+        Annotated[T2IAdapterConfig, T2IAdapterConfig.get_tag()],
+        Annotated[CLIPVisionDiffusersConfig, CLIPVisionDiffusersConfig.get_tag()],
+    ],
+    Discriminator(get_model_discriminator_value),
 ]
 
 AnyModelConfigValidator = TypeAdapter(AnyModelConfig)
 
 
-# IMPLEMENTATION NOTE:
-# The preferred alternative to the above is a discriminated Union as shown
-# below. However, it breaks FastAPI when used as the input Body parameter in a route.
-# This is a known issue. Please see:
-#   https://github.com/tiangolo/fastapi/discussions/9761 and
-#   https://github.com/tiangolo/fastapi/discussions/9287
-# AnyModelConfig = Annotated[
-#     Union[
-#         _MainModelConfig,
-#         _ONNXConfig,
-#         _VaeConfig,
-#         _ControlNetConfig,
-#         LoRAConfig,
-#         TextualInversionConfig,
-#         IPAdapterConfig,
-#         CLIPVisionDiffusersConfig,
-#         T2IConfig,
-#     ],
-#     Field(discriminator="type"),
-# ]
-
-
 class ModelConfigFactory(object):
     """Class for parsing config dicts into StableDiffusion Config obects."""
 
@@ -332,6 +406,6 @@ class ModelConfigFactory(object):
         assert model is not None
         if key:
             model.key = key
-        if timestamp:
-            model.last_modified = timestamp
+        if isinstance(model, CheckpointConfigBase) and timestamp is not None:
+            model.converted_at = timestamp
         return model  # type: ignore

invokeai/backend/model_manager/hash.py

@@ -12,6 +12,8 @@ import hashlib
 import os
 from pathlib import Path
 from typing import Callable, Literal, Optional, Union
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
 
 from blake3 import blake3
 
@@ -105,13 +107,14 @@ class ModelHash:
         """
         model_component_paths = self._get_file_paths(dir, self._file_filter)
 
-        component_hashes: list[str] = []
-        for component in sorted(model_component_paths):
-            component_hashes.append(self._hash_file(component))
+        # Use ThreadPoolExecutor to hash files in parallel
+        with ThreadPoolExecutor(min(((os.cpu_count() or 1) + 4), len(model_component_paths))) as executor:
+            future_to_component = {executor.submit(self._hash_file, component): component for component in sorted(model_component_paths)}
+            component_hashes = [future.result() for future in as_completed(future_to_component)]
 
-        # BLAKE3 is cryptographically secure. We may as well fall back on a secure algorithm
-        # for the composite hash
+        # BLAKE3 to hash the hashes
         composite_hasher = blake3()
+        component_hashes.sort()
         for h in component_hashes:
             composite_hasher.update(h.encode("utf-8"))
         return composite_hasher.hexdigest()
@@ -129,10 +132,12 @@ class ModelHash:
         """
 
         files: list[Path] = []
-        for root, _dirs, _files in os.walk(model_path):
-            for file in _files:
-                if file_filter(file):
-                    files.append(Path(root, file))
+        entries = [entry for entry in os.scandir(model_path.as_posix()) if not entry.name.startswith(".")]
+        dirs = [entry for entry in entries if entry.is_dir()]
+        file_paths = [entry.path for entry in entries if entry.is_file() and file_filter(entry.path)]
+        files.extend([Path(file) for file in file_paths])
+        for dir in dirs:
+            files.extend(ModelHash._get_file_paths(Path(dir.path), file_filter))
         return files
 
     @staticmethod
@@ -161,13 +166,11 @@ class ModelHash:
         """
 
         def hashlib_hasher(file_path: Path) -> str:
-            """Hashes a file using a hashlib algorithm. Uses `memoryview` to avoid reading the entire file into memory."""
+            """Hashes a file using a hashlib algorithm."""
             hasher = hashlib.new(algorithm)
-            buffer = bytearray(128 * 1024)
-            mv = memoryview(buffer)
-            with open(file_path, "rb", buffering=0) as f:
-                while n := f.readinto(mv):
-                    hasher.update(mv[:n])
+            with open(file_path, "rb") as f:
+                for chunk in iter(lambda: f.read(8 * 1024), b""):
+                    hasher.update(chunk)
             return hasher.hexdigest()
 
         return hashlib_hasher

invokeai/backend/model_manager/load/load_default.py

@@ -13,6 +13,7 @@ from invokeai.backend.model_manager import (
     ModelRepoVariant,
     SubModelType,
 )
+from invokeai.backend.model_manager.config import DiffusersConfigBase, ModelType
 from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
@@ -50,7 +51,7 @@ class ModelLoader(ModelLoaderBase):
         :param submodel_type: an ModelType enum indicating the portion of
                the model to retrieve (e.g. ModelType.Vae)
         """
-        if model_config.type == "main" and not submodel_type:
+        if model_config.type is ModelType.Main and not submodel_type:
             raise InvalidModelConfigException("submodel_type is required when loading a main model")
 
         model_path, model_config, submodel_type = self._get_model_path(model_config, submodel_type)
@@ -80,7 +81,7 @@ class ModelLoader(ModelLoaderBase):
         self._convert_cache.make_room(self.get_size_fs(config, model_path, submodel_type))
         return self._convert_model(config, model_path, cache_path)
 
-    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, cache_path: Path) -> bool:
+    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
         return False
 
     def _load_if_needed(
@@ -119,7 +120,7 @@ class ModelLoader(ModelLoaderBase):
         return calc_model_size_by_fs(
             model_path=model_path,
             subfolder=submodel_type.value if submodel_type else None,
-            variant=config.repo_variant if hasattr(config, "repo_variant") else None,
+            variant=config.repo_variant if isinstance(config, DiffusersConfigBase) else None,
         )
 
     # This needs to be implemented in subclasses that handle checkpoints

invokeai/backend/model_manager/load/model_loader_registry.py

@@ -15,10 +15,8 @@ Use like this:
 
 """
 
-import hashlib
 from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Callable, Dict, Optional, Tuple, Type
+from typing import Callable, Dict, Optional, Tuple, Type, TypeVar
 
 from ..config import (
     AnyModelConfig,
@@ -27,8 +25,6 @@ from ..config import (
     ModelFormat,
     ModelType,
     SubModelType,
-    VaeCheckpointConfig,
-    VaeDiffusersConfig,
 )
 from . import ModelLoaderBase
 
@@ -61,6 +57,9 @@ class ModelLoaderRegistryBase(ABC):
         """
 
 
+TModelLoader = TypeVar("TModelLoader", bound=ModelLoaderBase)
+
+
 class ModelLoaderRegistry:
     """
     This class allows model loaders to register their type, base and format.
@@ -71,10 +70,10 @@ class ModelLoaderRegistry:
     @classmethod
     def register(
         cls, type: ModelType, format: ModelFormat, base: BaseModelType = BaseModelType.Any
-    ) -> Callable[[Type[ModelLoaderBase]], Type[ModelLoaderBase]]:
+    ) -> Callable[[Type[TModelLoader]], Type[TModelLoader]]:
         """Define a decorator which registers the subclass of loader."""
 
-        def decorator(subclass: Type[ModelLoaderBase]) -> Type[ModelLoaderBase]:
+        def decorator(subclass: Type[TModelLoader]) -> Type[TModelLoader]:
             key = cls._to_registry_key(base, type, format)
             if key in cls._registry:
                 raise Exception(
@@ -90,33 +89,15 @@ class ModelLoaderRegistry:
         cls, config: AnyModelConfig, submodel_type: Optional[SubModelType]
     ) -> Tuple[Type[ModelLoaderBase], ModelConfigBase, Optional[SubModelType]]:
         """Get subclass of ModelLoaderBase registered to handle base and type."""
-        # We have to handle VAE overrides here because this will change the model type and the corresponding implementation returned
-        conf2, submodel_type = cls._handle_subtype_overrides(config, submodel_type)
 
-        key1 = cls._to_registry_key(conf2.base, conf2.type, conf2.format)  # for a specific base type
-        key2 = cls._to_registry_key(BaseModelType.Any, conf2.type, conf2.format)  # with wildcard Any
+        key1 = cls._to_registry_key(config.base, config.type, config.format)  # for a specific base type
+        key2 = cls._to_registry_key(BaseModelType.Any, config.type, config.format)  # with wildcard Any
         implementation = cls._registry.get(key1) or cls._registry.get(key2)
         if not implementation:
             raise NotImplementedError(
                 f"No subclass of LoadedModel is registered for base={config.base}, type={config.type}, format={config.format}"
             )
-        return implementation, conf2, submodel_type
-
-    @classmethod
-    def _handle_subtype_overrides(
-        cls, config: AnyModelConfig, submodel_type: Optional[SubModelType]
-    ) -> Tuple[ModelConfigBase, Optional[SubModelType]]:
-        if submodel_type == SubModelType.Vae and hasattr(config, "vae") and config.vae is not None:
-            model_path = Path(config.vae)
-            config_class = (
-                VaeCheckpointConfig if model_path.suffix in [".pt", ".safetensors", ".ckpt"] else VaeDiffusersConfig
-            )
-            hash = hashlib.md5(model_path.as_posix().encode("utf-8")).hexdigest()
-            new_conf = config_class(path=model_path.as_posix(), name=model_path.stem, base=config.base, key=hash)
-            submodel_type = None
-        else:
-            new_conf = config
-        return new_conf, submodel_type
+        return implementation, config, submodel_type
 
     @staticmethod
     def _to_registry_key(base: BaseModelType, type: ModelType, format: ModelFormat) -> str:

invokeai/backend/model_manager/load/model_loaders/controlnet.py

@@ -3,8 +3,8 @@
 
 from pathlib import Path
 
-import safetensors
 import torch
+from safetensors.torch import load_file as safetensors_load_file
 
 from invokeai.backend.model_manager import (
     AnyModelConfig,
@@ -12,6 +12,7 @@ from invokeai.backend.model_manager import (
     ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.config import CheckpointConfigBase
 from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_controlnet_to_diffusers
 
 from .. import ModelLoaderRegistry
@@ -20,15 +21,15 @@ from .generic_diffusers import GenericDiffusersLoader
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
-class ControlnetLoader(GenericDiffusersLoader):
+class ControlNetLoader(GenericDiffusersLoader):
     """Class to load ControlNet models."""
 
     def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        if config.format != ModelFormat.Checkpoint:
+        if not isinstance(config, CheckpointConfigBase):
             return False
         elif (
             dest_path.exists()
-            and (dest_path / "config.json").stat().st_mtime >= (config.last_modified or 0.0)
+            and (dest_path / "config.json").stat().st_mtime >= (config.converted_at or 0.0)
             and (dest_path / "config.json").stat().st_mtime >= model_path.stat().st_mtime
         ):
             return False
@@ -37,13 +38,13 @@ class ControlnetLoader(GenericDiffusersLoader):
 
     def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
         if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
-            raise Exception(f"Vae conversion not supported for model type: {config.base}")
+            raise Exception(f"ControlNet conversion not supported for model type: {config.base}")
         else:
-            assert hasattr(config, "config")
-            config_file = config.config
+            assert isinstance(config, CheckpointConfigBase)
+            config_file = config.config_path
 
         if model_path.suffix == ".safetensors":
-            checkpoint = safetensors.torch.load_file(model_path, device="cpu")
+            checkpoint = safetensors_load_file(model_path, device="cpu")
         else:
             checkpoint = torch.load(model_path, map_location="cpu")
 

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -3,9 +3,10 @@
 
 import sys
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
-from diffusers import ConfigMixin, ModelMixin
+from diffusers.configuration_utils import ConfigMixin
+from diffusers.models.modeling_utils import ModelMixin
 
 from invokeai.backend.model_manager import (
     AnyModel,
@@ -41,6 +42,7 @@ class GenericDiffusersLoader(ModelLoader):
     # TO DO: Add exception handling
     def get_hf_load_class(self, model_path: Path, submodel_type: Optional[SubModelType] = None) -> ModelMixin:
         """Given the model path and submodel, returns the diffusers ModelMixin subclass needed to load."""
+        result = None
         if submodel_type:
             try:
                 config = self._load_diffusers_config(model_path, config_name="model_index.json")
@@ -64,6 +66,7 @@ class GenericDiffusersLoader(ModelLoader):
                     raise InvalidModelConfigException("Unable to decifer Load Class based on given config.json")
             except KeyError as e:
                 raise InvalidModelConfigException("An expected config.json file is missing from this model.") from e
+        assert result is not None
         return result
 
     # TO DO: Add exception handling
@@ -75,7 +78,7 @@ class GenericDiffusersLoader(ModelLoader):
         result: ModelMixin = getattr(res_type, class_name)
         return result
 
-    def _load_diffusers_config(self, model_path: Path, config_name: str = "config.json") -> Dict[str, Any]:
+    def _load_diffusers_config(self, model_path: Path, config_name: str = "config.json") -> dict[str, Any]:
         return ConfigLoader.load_config(model_path, config_name=config_name)
 
 
@@ -83,8 +86,8 @@ class ConfigLoader(ConfigMixin):
     """Subclass of ConfigMixin for loading diffusers configuration files."""
 
     @classmethod
-    def load_config(cls, *args: Any, **kwargs: Any) -> Dict[str, Any]:
+    def load_config(cls, *args: Any, **kwargs: Any) -> dict[str, Any]:  # pyright: ignore [reportIncompatibleMethodOverride]
         """Load a diffusrs ConfigMixin configuration."""
         cls.config_name = kwargs.pop("config_name")
-        # Diffusers doesn't provide typing info
+        # TODO(psyche): the types on this diffusers method are not correct
         return super().load_config(*args, **kwargs)  # type: ignore

invokeai/backend/model_manager/load/model_loaders/ip_adapter.py

@@ -31,7 +31,7 @@ class IPAdapterInvokeAILoader(ModelLoader):
         if submodel_type is not None:
             raise ValueError("There are no submodels in an IP-Adapter model.")
         model = build_ip_adapter(
-            ip_adapter_ckpt_path=model_path / "ip_adapter.bin",
+            ip_adapter_ckpt_path=str(model_path / "ip_adapter.bin"),
             device=torch.device("cpu"),
             dtype=self._torch_dtype,
         )

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -22,9 +22,9 @@ from invokeai.backend.model_manager.load.model_cache.model_cache_base import Mod
 from .. import ModelLoader, ModelLoaderRegistry
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Lora, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Lora, format=ModelFormat.Lycoris)
-class LoraLoader(ModelLoader):
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS)
+class LoRALoader(ModelLoader):
     """Class to load LoRA models."""
 
     # We cheat a little bit to get access to the model base

invokeai/backend/model_manager/load/model_loaders/onnx.py

@@ -18,7 +18,7 @@ from .. import ModelLoaderRegistry
 from .generic_diffusers import GenericDiffusersLoader
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ONNX, format=ModelFormat.Onnx)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ONNX, format=ModelFormat.ONNX)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ONNX, format=ModelFormat.Olive)
 class OnnyxDiffusersModel(GenericDiffusersLoader):
     """Class to load onnx models."""

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -4,7 +4,8 @@
 from pathlib import Path
 from typing import Optional
 
-from diffusers import StableDiffusionInpaintPipeline, StableDiffusionPipeline
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
 
 from invokeai.backend.model_manager import (
     AnyModel,
@@ -16,7 +17,7 @@ from invokeai.backend.model_manager import (
     ModelVariantType,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import MainCheckpointConfig
+from invokeai.backend.model_manager.config import CheckpointConfigBase, MainCheckpointConfig
 from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
 
 from .. import ModelLoaderRegistry
@@ -54,11 +55,11 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
         return result
 
     def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        if config.format != ModelFormat.Checkpoint:
+        if not isinstance(config, CheckpointConfigBase):
             return False
         elif (
             dest_path.exists()
-            and (dest_path / "model_index.json").stat().st_mtime >= (config.last_modified or 0.0)
+            and (dest_path / "model_index.json").stat().st_mtime >= (config.converted_at or 0.0)
             and (dest_path / "model_index.json").stat().st_mtime >= model_path.stat().st_mtime
         ):
             return False
@@ -73,7 +74,7 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
             StableDiffusionInpaintPipeline if variant == ModelVariantType.Inpaint else StableDiffusionPipeline
         )
 
-        config_file = config.config
+        config_file = config.config_path
 
         self._logger.info(f"Converting {model_path} to diffusers format")
         convert_ckpt_to_diffusers(

invokeai/backend/model_manager/load/model_loaders/vae.py

@@ -3,9 +3,9 @@
 
 from pathlib import Path
 
-import safetensors
 import torch
 from omegaconf import DictConfig, OmegaConf
+from safetensors.torch import load_file as safetensors_load_file
 
 from invokeai.backend.model_manager import (
     AnyModelConfig,
@@ -13,24 +13,25 @@ from invokeai.backend.model_manager import (
     ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.config import CheckpointConfigBase
 from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ldm_vae_to_diffusers
 
 from .. import ModelLoaderRegistry
 from .generic_diffusers import GenericDiffusersLoader
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Vae, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Vae, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Vae, format=ModelFormat.Checkpoint)
-class VaeLoader(GenericDiffusersLoader):
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.VAE, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.VAE, format=ModelFormat.Checkpoint)
+class VAELoader(GenericDiffusersLoader):
     """Class to load VAE models."""
 
     def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        if config.format != ModelFormat.Checkpoint:
+        if not isinstance(config, CheckpointConfigBase):
             return False
         elif (
             dest_path.exists()
-            and (dest_path / "config.json").stat().st_mtime >= (config.last_modified or 0.0)
+            and (dest_path / "config.json").stat().st_mtime >= (config.converted_at or 0.0)
             and (dest_path / "config.json").stat().st_mtime >= model_path.stat().st_mtime
         ):
             return False
@@ -38,16 +39,15 @@ class VaeLoader(GenericDiffusersLoader):
             return True
 
     def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
-        # TO DO: check whether sdxl VAE models convert.
+        # TODO(MM2): check whether sdxl VAE models convert.
         if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
-            raise Exception(f"Vae conversion not supported for model type: {config.base}")
+            raise Exception(f"VAE conversion not supported for model type: {config.base}")
         else:
-            config_file = (
-                "v1-inference.yaml" if config.base == BaseModelType.StableDiffusion1 else "v2-inference-v.yaml"
-            )
+            assert isinstance(config, CheckpointConfigBase)
+            config_file = config.config_path
 
         if model_path.suffix == ".safetensors":
-            checkpoint = safetensors.torch.load_file(model_path, device="cpu")
+            checkpoint = safetensors_load_file(model_path, device="cpu")
         else:
             checkpoint = torch.load(model_path, map_location="cpu")
 
@@ -55,7 +55,7 @@ class VaeLoader(GenericDiffusersLoader):
         if "state_dict" in checkpoint:
             checkpoint = checkpoint["state_dict"]
 
-        ckpt_config = OmegaConf.load(self._app_config.legacy_conf_path / config_file)
+        ckpt_config = OmegaConf.load(self._app_config.root_path / config_file)
         assert isinstance(ckpt_config, DictConfig)
 
         vae_model = convert_ldm_vae_to_diffusers(

invokeai/backend/model_manager/merge.py

@@ -16,6 +16,7 @@ from diffusers import AutoPipelineForText2Image
 from diffusers.utils import logging as dlogging
 
 from invokeai.app.services.model_install import ModelInstallServiceBase
+from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
 from invokeai.backend.util.devices import choose_torch_device, torch_dtype
 
 from . import (
@@ -117,7 +118,6 @@ class ModelMerger(object):
         config = self._installer.app_config
         store = self._installer.record_store
         base_models: Set[BaseModelType] = set()
-        vae = None
         variant = None if self._installer.app_config.full_precision else "fp16"
 
         assert (
@@ -134,10 +134,6 @@ class ModelMerger(object):
                 "normal"
             ), f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
 
-            # pick up the first model's vae
-            if key == model_keys[0]:
-                vae = info.vae
-
             # tally base models used
             base_models.add(info.base)
             model_paths.extend([config.models_path / info.path])
@@ -163,12 +159,10 @@ class ModelMerger(object):
 
         # update model's config
         model_config = self._installer.record_store.get_model(key)
-        model_config.update(
-            {
-                "name": merged_model_name,
-                "description": f"Merge of models {', '.join(model_names)}",
-                "vae": vae,
-            }
+        model_config.name = merged_model_name
+        model_config.description = f"Merge of models {', '.join(model_names)}"
+
+        self._installer.record_store.update_model(
+            key, ModelRecordChanges(name=model_config.name, description=model_config.description)
         )
-        self._installer.record_store.update_model(key, model_config)
         return model_config

invokeai/backend/model_manager/metadata/__init__.py

@@ -25,9 +25,7 @@ from .metadata_base import (
     AnyModelRepoMetadataValidator,
     BaseMetadata,
     CivitaiMetadata,
-    CommercialUsage,
     HuggingFaceMetadata,
-    LicenseRestrictions,
     ModelMetadataWithFiles,
     RemoteModelFile,
     UnknownMetadataException,
@@ -38,10 +36,8 @@ __all__ = [
     "AnyModelRepoMetadataValidator",
     "CivitaiMetadata",
     "CivitaiMetadataFetch",
-    "CommercialUsage",
     "HuggingFaceMetadata",
     "HuggingFaceMetadataFetch",
-    "LicenseRestrictions",
     "ModelMetadataFetchBase",
     "BaseMetadata",
     "ModelMetadataWithFiles",

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

@@ -23,22 +23,21 @@ metadata = fetcher.from_url("https://civitai.com/models/206883/split")
 print(metadata.trained_words)
 """
 
+import json
 import re
-from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 import requests
+from pydantic import TypeAdapter, ValidationError
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 
-from invokeai.backend.model_manager import ModelRepoVariant
+from invokeai.backend.model_manager.config import ModelRepoVariant
 
 from ..metadata_base import (
     AnyModelRepoMetadata,
     CivitaiMetadata,
-    CommercialUsage,
-    LicenseRestrictions,
     RemoteModelFile,
     UnknownMetadataException,
 )
@@ -52,10 +51,13 @@ CIVITAI_VERSION_ENDPOINT = "https://civitai.com/api/v1/model-versions/"
 CIVITAI_MODEL_ENDPOINT = "https://civitai.com/api/v1/models/"
 
 
+StringSetAdapter = TypeAdapter(set[str])
+
+
 class CivitaiMetadataFetch(ModelMetadataFetchBase):
     """Fetch model metadata from Civitai."""
 
-    def __init__(self, session: Optional[Session] = None):
+    def __init__(self, session: Optional[Session] = None, api_key: Optional[str] = None):
         """
         Initialize the fetcher with an optional requests.sessions.Session object.
 
@@ -63,6 +65,7 @@ class CivitaiMetadataFetch(ModelMetadataFetchBase):
         this module without an internet connection.
         """
         self._requests = session or requests.Session()
+        self._api_key = api_key
 
     def from_url(self, url: AnyHttpUrl) -> AnyModelRepoMetadata:
         """
@@ -102,22 +105,21 @@ class CivitaiMetadataFetch(ModelMetadataFetchBase):
         May raise an `UnknownMetadataException`.
         """
         model_url = CIVITAI_MODEL_ENDPOINT + str(model_id)
-        model_json = self._requests.get(model_url).json()
-        return self._from_model_json(model_json)
+        model_json = self._requests.get(self._get_url_with_api_key(model_url)).json()
+        return self._from_api_response(model_json)
 
-    def _from_model_json(self, model_json: Dict[str, Any], version_id: Optional[int] = None) -> CivitaiMetadata:
+    def _from_api_response(self, api_response: dict[str, Any], version_id: Optional[int] = None) -> CivitaiMetadata:
         try:
-            version_id = version_id or model_json["modelVersions"][0]["id"]
+            version_id = version_id or api_response["modelVersions"][0]["id"]
         except TypeError as excp:
             raise UnknownMetadataException from excp
 
         # loop till we find the section containing the version requested
-        version_sections = [x for x in model_json["modelVersions"] if x["id"] == version_id]
+        version_sections = [x for x in api_response["modelVersions"] if x["id"] == version_id]
         if not version_sections:
             raise UnknownMetadataException(f"Version {version_id} not found in model metadata")
 
         version_json = version_sections[0]
-        safe_thumbnails = [x["url"] for x in version_json["images"] if x["nsfw"] == "None"]
 
         # Civitai has one "primary" file plus others such as VAEs. We only fetch the primary.
         primary = [x for x in version_json["files"] if x.get("primary")]
@@ -134,36 +136,23 @@ class CivitaiMetadataFetch(ModelMetadataFetchBase):
             url = url + f"?type={primary_file['type']}{metadata_string}"
         model_files = [
             RemoteModelFile(
-                url=url,
+                url=self._get_url_with_api_key(url),
                 path=Path(primary_file["name"]),
                 size=int(primary_file["sizeKB"] * 1024),
                 sha256=primary_file["hashes"]["SHA256"],
             )
         ]
+
+        try:
+            trigger_phrases = StringSetAdapter.validate_python(version_json.get("trainedWords"))
+        except ValidationError:
+            trigger_phrases: set[str] = set()
+
         return CivitaiMetadata(
-            id=model_json["id"],
             name=version_json["name"],
-            version_id=version_json["id"],
-            version_name=version_json["name"],
-            created=datetime.fromisoformat(_fix_timezone(version_json["createdAt"])),
-            updated=datetime.fromisoformat(_fix_timezone(version_json["updatedAt"])),
-            published=datetime.fromisoformat(_fix_timezone(version_json["publishedAt"])),
-            base_model_trained_on=version_json["baseModel"],  # note - need a dictionary to turn into a BaseModelType
             files=model_files,
-            download_url=version_json["downloadUrl"],
-            thumbnail_url=safe_thumbnails[0] if safe_thumbnails else None,
-            author=model_json["creator"]["username"],
-            description=model_json["description"],
-            version_description=version_json["description"] or "",
-            tags=model_json["tags"],
-            trained_words=version_json["trainedWords"],
-            nsfw=model_json["nsfw"],
-            restrictions=LicenseRestrictions(
-                AllowNoCredit=model_json["allowNoCredit"],
-                AllowCommercialUse={CommercialUsage(x) for x in model_json["allowCommercialUse"]},
-                AllowDerivatives=model_json["allowDerivatives"],
-                AllowDifferentLicense=model_json["allowDifferentLicense"],
-            ),
+            trigger_phrases=trigger_phrases,
+            api_response=json.dumps(version_json),
         )
 
     def from_civitai_versionid(self, version_id: int, model_id: Optional[int] = None) -> CivitaiMetadata:
@@ -174,14 +163,14 @@ class CivitaiMetadataFetch(ModelMetadataFetchBase):
         """
         if model_id is None:
             version_url = CIVITAI_VERSION_ENDPOINT + str(version_id)
-            version = self._requests.get(version_url).json()
+            version = self._requests.get(self._get_url_with_api_key(version_url)).json()
             if error := version.get("error"):
                 raise UnknownMetadataException(error)
             model_id = version["modelId"]
 
         model_url = CIVITAI_MODEL_ENDPOINT + str(model_id)
-        model_json = self._requests.get(model_url).json()
-        return self._from_model_json(model_json, version_id)
+        model_json = self._requests.get(self._get_url_with_api_key(model_url)).json()
+        return self._from_api_response(model_json, version_id)
 
     @classmethod
     def from_json(cls, json: str) -> CivitaiMetadata:
@@ -189,6 +178,11 @@ class CivitaiMetadataFetch(ModelMetadataFetchBase):
         metadata = CivitaiMetadata.model_validate_json(json)
         return metadata
 
+    def _get_url_with_api_key(self, url: str) -> str:
+        if not self._api_key:
+            return url
 
-def _fix_timezone(date: str) -> str:
-    return re.sub(r"Z$", "+00:00", date)
+        if "?" in url:
+            return f"{url}&token={self._api_key}"
+
+        return f"{url}?token={self._api_key}"

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

@@ -13,6 +13,7 @@ metadata = fetcher.from_url("https://huggingface.co/stabilityai/sdxl-turbo")
 print(metadata.tags)
 """
 
+import json
 import re
 from pathlib import Path
 from typing import Optional
@@ -23,7 +24,7 @@ from huggingface_hub.utils._errors import RepositoryNotFoundError, RevisionNotFo
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 
-from invokeai.backend.model_manager import ModelRepoVariant
+from invokeai.backend.model_manager.config import ModelRepoVariant
 
 from ..metadata_base import (
     AnyModelRepoMetadata,
@@ -60,6 +61,7 @@ class HuggingFaceMetadataFetch(ModelMetadataFetchBase):
         # Little loop which tries fetching a revision corresponding to the selected variant.
         # If not available, then set variant to None and get the default.
         # If this too fails, raise exception.
+
         model_info = None
         while not model_info:
             try:
@@ -72,23 +74,24 @@ class HuggingFaceMetadataFetch(ModelMetadataFetchBase):
                 else:
                     variant = None
 
+        files: list[RemoteModelFile] = []
+
         _, name = id.split("/")
-        return HuggingFaceMetadata(
-            id=model_info.id,
-            author=model_info.author,
-            name=name,
-            last_modified=model_info.last_modified,
-            tag_dict=model_info.card_data.to_dict() if model_info.card_data else {},
-            tags=model_info.tags,
-            files=[
+
+        for s in model_info.siblings or []:
+            assert s.rfilename is not None
+            assert s.size is not None
+            files.append(
                 RemoteModelFile(
-                    url=hf_hub_url(id, x.rfilename, revision=variant),
-                    path=Path(name, x.rfilename),
-                    size=x.size,
-                    sha256=x.lfs.get("sha256") if x.lfs else None,
+                    url=hf_hub_url(id, s.rfilename, revision=variant),
+                    path=Path(name, s.rfilename),
+                    size=s.size,
+                    sha256=s.lfs.get("sha256") if s.lfs else None,
                 )
-                for x in model_info.siblings
-            ],
+            )
+
+        return HuggingFaceMetadata(
+            id=model_info.id, name=name, files=files, api_response=json.dumps(model_info.__dict__, default=str)
         )
 
     def from_url(self, url: AnyHttpUrl) -> AnyModelRepoMetadata:

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -14,10 +14,8 @@ versions of these fields are intended to be kept in sync with the
 remote repo.
 """
 
-from datetime import datetime
-from enum import Enum
 from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Union
+from typing import List, Literal, Optional, Union
 
 from huggingface_hub import configure_http_backend, hf_hub_url
 from pydantic import BaseModel, Field, TypeAdapter
@@ -25,7 +23,6 @@ from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 from typing_extensions import Annotated
 
-from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
 from invokeai.backend.model_manager import ModelRepoVariant
 
 from ..util import select_hf_files
@@ -35,31 +32,6 @@ class UnknownMetadataException(Exception):
     """Raised when no metadata is available for a model."""
 
 
-class CommercialUsage(str, Enum):
-    """Type of commercial usage allowed."""
-
-    No = "None"
-    Image = "Image"
-    Rent = "Rent"
-    RentCivit = "RentCivit"
-    Sell = "Sell"
-
-
-class LicenseRestrictions(BaseModel):
-    """Broad categories of licensing restrictions."""
-
-    AllowNoCredit: bool = Field(
-        description="if true, model can be redistributed without crediting author", default=False
-    )
-    AllowDerivatives: bool = Field(description="if true, derivatives of this model can be redistributed", default=False)
-    AllowDifferentLicense: bool = Field(
-        description="if true, derivatives of this model be redistributed under a different license", default=False
-    )
-    AllowCommercialUse: Optional[Set[CommercialUsage] | CommercialUsage] = Field(
-        description="Type of commercial use allowed if no commercial use is allowed.", default=None
-    )
-
-
 class RemoteModelFile(BaseModel):
     """Information about a downloadable file that forms part of a model."""
 
@@ -69,24 +41,10 @@ class RemoteModelFile(BaseModel):
     sha256: Optional[str] = Field(description="SHA256 hash of this model (not always available)", default=None)
 
 
-class ModelDefaultSettings(BaseModel):
-    vae: str | None
-    vae_precision: str | None
-    scheduler: SCHEDULER_NAME_VALUES | None
-    steps: int | None
-    cfg_scale: float | None
-    cfg_rescale_multiplier: float | None
-
-
 class ModelMetadataBase(BaseModel):
     """Base class for model metadata information."""
 
     name: str = Field(description="model's name")
-    author: str = Field(description="model's author")
-    tags: Optional[Set[str]] = Field(description="tags provided by model source", default=None)
-    default_settings: Optional[ModelDefaultSettings] = Field(
-        description="default settings for this model", default=None
-    )
 
 
 class BaseMetadata(ModelMetadataBase):
@@ -124,60 +82,16 @@ class CivitaiMetadata(ModelMetadataWithFiles):
     """Extended metadata fields provided by Civitai."""
 
     type: Literal["civitai"] = "civitai"
-    id: int = Field(description="Civitai version identifier")
-    version_name: str = Field(description="Version identifier, such as 'V2-alpha'")
-    version_id: int = Field(description="Civitai model version identifier")
-    created: datetime = Field(description="date the model was created")
-    updated: datetime = Field(description="date the model was last modified")
-    published: datetime = Field(description="date the model was published to Civitai")
-    description: str = Field(description="text description of model; may contain HTML")
-    version_description: str = Field(
-        description="text description of the model's reversion; usually change history; may contain HTML"
-    )
-    nsfw: bool = Field(description="whether the model tends to generate NSFW content", default=False)
-    restrictions: LicenseRestrictions = Field(description="license terms", default_factory=LicenseRestrictions)
-    trained_words: Set[str] = Field(description="words to trigger the model", default_factory=set)
-    download_url: AnyHttpUrl = Field(description="download URL for this model")
-    base_model_trained_on: str = Field(description="base model on which this model was trained (currently not an enum)")
-    thumbnail_url: Optional[AnyHttpUrl] = Field(description="a thumbnail image for this model", default=None)
-    weight_minmax: Tuple[float, float] = Field(
-        description="minimum and maximum slider values for a LoRA or other secondary model", default=(-1.0, +2.0)
-    )  # note: For future use
-
-    @property
-    def credit_required(self) -> bool:
-        """Return True if you must give credit for derivatives of this model and images generated from it."""
-        return not self.restrictions.AllowNoCredit
-
-    @property
-    def allow_commercial_use(self) -> bool:
-        """Return True if commercial use is allowed."""
-        if self.restrictions.AllowCommercialUse is None:
-            return False
-        else:
-            # accommodate schema change
-            acu = self.restrictions.AllowCommercialUse
-            commercial_usage = acu if isinstance(acu, set) else {acu}
-            return CommercialUsage.No not in commercial_usage
-
-    @property
-    def allow_derivatives(self) -> bool:
-        """Return True if derivatives of this model can be redistributed."""
-        return self.restrictions.AllowDerivatives
-
-    @property
-    def allow_different_license(self) -> bool:
-        """Return true if derivatives of this model can use a different license."""
-        return self.restrictions.AllowDifferentLicense
+    trigger_phrases: set[str] = Field(description="Trigger phrases extracted from the API response")
+    api_response: Optional[str] = Field(description="Response from the Civitai API as stringified JSON", default=None)
 
 
 class HuggingFaceMetadata(ModelMetadataWithFiles):
     """Extended metadata fields provided by HuggingFace."""
 
     type: Literal["huggingface"] = "huggingface"
-    id: str = Field(description="huggingface model id")
-    tag_dict: Dict[str, Any]
-    last_modified: datetime = Field(description="date of last commit to repo")
+    id: str = Field(description="The HF model id")
+    api_response: Optional[str] = Field(description="Response from the HF API as stringified JSON", default=None)
 
     def download_urls(
         self,
@@ -206,7 +120,7 @@ class HuggingFaceMetadata(ModelMetadataWithFiles):
         # the next step reads model_index.json to determine which subdirectories belong
         # to the model
         if Path(f"{prefix}model_index.json") in paths:
-            url = hf_hub_url(self.id, filename="model_index.json", subfolder=subfolder)
+            url = hf_hub_url(self.id, filename="model_index.json", subfolder=str(subfolder) if subfolder else None)
             resp = session.get(url)
             resp.raise_for_status()
             submodels = resp.json()

invokeai/backend/model_manager/metadata/metadata_store.py

@@ -1,221 +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 .fetch import ModelMetadataFetchBase
-from .metadata_base import AnyModelRepoMetadata, UnknownMetadataException
-
-
-class ModelMetadataStore:
-    """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/backend/model_manager/probe.py

@@ -8,6 +8,7 @@ import torch
 from picklescan.scanner import scan_file_path
 
 import invokeai.backend.util.logging as logger
+from invokeai.app.util.misc import uuid_string
 from invokeai.backend.util.util import SilenceWarnings
 
 from .config import (
@@ -17,6 +18,7 @@ from .config import (
     ModelConfigFactory,
     ModelFormat,
     ModelRepoVariant,
+    ModelSourceType,
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
@@ -82,6 +84,9 @@ class ProbeBase(object):
 
 
 class ModelProbe(object):
+
+    hasher = ModelHash()
+
     PROBES: Dict[str, Dict[ModelType, type[ProbeBase]]] = {
         "diffusers": {},
         "checkpoint": {},
@@ -95,8 +100,8 @@ class ModelProbe(object):
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
         "LatentConsistencyModelPipeline": ModelType.Main,
-        "AutoencoderKL": ModelType.Vae,
-        "AutoencoderTiny": ModelType.Vae,
+        "AutoencoderKL": ModelType.VAE,
+        "AutoencoderTiny": ModelType.VAE,
         "ControlNetModel": ModelType.ControlNet,
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
         "T2IAdapter": ModelType.T2IAdapter,
@@ -108,14 +113,6 @@ class ModelProbe(object):
     ) -> None:
         cls.PROBES[format][model_type] = probe_class
 
-    @classmethod
-    def heuristic_probe(
-        cls,
-        model_path: Path,
-        fields: Optional[Dict[str, Any]] = None,
-    ) -> AnyModelConfig:
-        return cls.probe(model_path, fields)
-
     @classmethod
     def probe(
         cls,
@@ -137,19 +134,21 @@ class ModelProbe(object):
         format_type = ModelFormat.Diffusers if model_path.is_dir() else ModelFormat.Checkpoint
         model_info = None
         model_type = None
-        if format_type == "diffusers":
+        if format_type is ModelFormat.Diffusers:
             model_type = cls.get_model_type_from_folder(model_path)
         else:
             model_type = cls.get_model_type_from_checkpoint(model_path)
-        format_type = ModelFormat.Onnx if model_type == ModelType.ONNX else format_type
+        format_type = ModelFormat.ONNX if model_type == ModelType.ONNX else format_type
 
         probe_class = cls.PROBES[format_type].get(model_type)
         if not probe_class:
             raise InvalidModelConfigException(f"Unhandled combination of {format_type} and {model_type}")
 
-        hash = ModelHash().hash(model_path)
         probe = probe_class(model_path)
 
+        fields["source_type"] = fields.get("source_type") or ModelSourceType.Path
+        fields["source"] = fields.get("source") or model_path.as_posix()
+        fields["key"] = fields.get("key", uuid_string())
         fields["path"] = model_path.as_posix()
         fields["type"] = fields.get("type") or model_type
         fields["base"] = fields.get("base") or probe.get_base_type()
@@ -161,15 +160,17 @@ class ModelProbe(object):
             fields.get("description") or f"{fields['base'].value} {fields['type'].value} model {fields['name']}"
         )
         fields["format"] = fields.get("format") or probe.get_format()
-        fields["original_hash"] = fields.get("original_hash") or hash
-        fields["current_hash"] = fields.get("current_hash") or hash
+        fields["hash"] = fields.get("hash") or cls.hasher.hash(model_path)
 
-        if format_type == ModelFormat.Diffusers and hasattr(probe, "get_repo_variant"):
+        if format_type == ModelFormat.Diffusers and isinstance(probe, FolderProbeBase):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
 
         # additional fields needed for main and controlnet models
-        if fields["type"] in [ModelType.Main, ModelType.ControlNet] and fields["format"] == ModelFormat.Checkpoint:
-            fields["config"] = cls._get_checkpoint_config_path(
+        if (
+            fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE]
+            and fields["format"] is ModelFormat.Checkpoint
+        ):
+            fields["config_path"] = cls._get_checkpoint_config_path(
                 model_path,
                 model_type=fields["type"],
                 base_type=fields["base"],
@@ -179,7 +180,7 @@ class ModelProbe(object):
 
         # additional fields needed for main non-checkpoint models
         elif fields["type"] == ModelType.Main and fields["format"] in [
-            ModelFormat.Onnx,
+            ModelFormat.ONNX,
             ModelFormat.Olive,
             ModelFormat.Diffusers,
         ]:
@@ -213,11 +214,11 @@ class ModelProbe(object):
             if any(key.startswith(v) for v in {"cond_stage_model.", "first_stage_model.", "model.diffusion_model."}):
                 return ModelType.Main
             elif any(key.startswith(v) for v in {"encoder.conv_in", "decoder.conv_in"}):
-                return ModelType.Vae
+                return ModelType.VAE
             elif any(key.startswith(v) for v in {"lora_te_", "lora_unet_"}):
-                return ModelType.Lora
+                return ModelType.LoRA
             elif any(key.endswith(v) for v in {"to_k_lora.up.weight", "to_q_lora.down.weight"}):
-                return ModelType.Lora
+                return ModelType.LoRA
             elif any(key.startswith(v) for v in {"control_model", "input_blocks"}):
                 return ModelType.ControlNet
             elif key in {"emb_params", "string_to_param"}:
@@ -239,7 +240,7 @@ class ModelProbe(object):
             if (folder_path / f"learned_embeds.{suffix}").exists():
                 return ModelType.TextualInversion
             if (folder_path / f"pytorch_lora_weights.{suffix}").exists():
-                return ModelType.Lora
+                return ModelType.LoRA
         if (folder_path / "unet/model.onnx").exists():
             return ModelType.ONNX
         if (folder_path / "image_encoder.txt").exists():
@@ -285,13 +286,21 @@ class ModelProbe(object):
         if possible_conf.exists():
             return possible_conf.absolute()
 
-        if model_type == ModelType.Main:
+        if model_type is ModelType.Main:
             config_file = LEGACY_CONFIGS[base_type][variant_type]
             if isinstance(config_file, dict):  # need another tier for sd-2.x models
                 config_file = config_file[prediction_type]
-        elif model_type == ModelType.ControlNet:
+        elif model_type is ModelType.ControlNet:
             config_file = (
-                "../controlnet/cldm_v15.yaml" if base_type == BaseModelType("sd-1") else "../controlnet/cldm_v21.yaml"
+                "../controlnet/cldm_v15.yaml"
+                if base_type is BaseModelType.StableDiffusion1
+                else "../controlnet/cldm_v21.yaml"
+            )
+        elif model_type is ModelType.VAE:
+            config_file = (
+                "../stable-diffusion/v1-inference.yaml"
+                if base_type is BaseModelType.StableDiffusion1
+                else "../stable-diffusion/v2-inference.yaml"
             )
         else:
             raise InvalidModelConfigException(
@@ -497,12 +506,12 @@ class FolderProbeBase(ProbeBase):
             if ".fp16" in x.suffixes:
                 return ModelRepoVariant.FP16
             if "openvino_model" in x.name:
-                return ModelRepoVariant.OPENVINO
+                return ModelRepoVariant.OpenVINO
             if "flax_model" in x.name:
-                return ModelRepoVariant.FLAX
+                return ModelRepoVariant.Flax
             if x.suffix == ".onnx":
                 return ModelRepoVariant.ONNX
-        return ModelRepoVariant.DEFAULT
+        return ModelRepoVariant.Default
 
 
 class PipelineFolderProbe(FolderProbeBase):
@@ -708,8 +717,8 @@ class T2IAdapterFolderProbe(FolderProbeBase):
 
 ############## register probe classes ######
 ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.Lora, LoRAFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.VAE, VaeFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.LoRA, LoRAFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
@@ -717,8 +726,8 @@ ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderPro
 ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
 
 ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
-ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.VAE, VaeCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.LoRA, LoRACheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpointProbe)

invokeai/backend/model_manager/util/select_hf_files.py

@@ -13,6 +13,7 @@ files_to_download = select_hf_model_files(metadata.files, variant='onnx')
 """
 
 import re
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Dict, List, Optional, Set
 
@@ -34,7 +35,7 @@ def filter_files(
     The file list can be obtained from the `files` field of HuggingFaceMetadata,
     as defined in `invokeai.backend.model_manager.metadata.metadata_base`.
     """
-    variant = variant or ModelRepoVariant.DEFAULT
+    variant = variant or ModelRepoVariant.Default
     paths: List[Path] = []
     root = files[0].parts[0]
 
@@ -73,64 +74,81 @@ def filter_files(
     return sorted(_filter_by_variant(paths, variant))
 
 
+@dataclass
+class SubfolderCandidate:
+    path: Path
+    score: int
+
+
 def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path]:
     """Select the proper variant files from a list of HuggingFace repo_id paths."""
-    result = set()
-    basenames: Dict[Path, Path] = {}
+    result: set[Path] = set()
+    subfolder_weights: dict[Path, list[SubfolderCandidate]] = {}
     for path in files:
         if path.suffix in [".onnx", ".pb", ".onnx_data"]:
             if variant == ModelRepoVariant.ONNX:
                 result.add(path)
 
         elif "openvino_model" in path.name:
-            if variant == ModelRepoVariant.OPENVINO:
+            if variant == ModelRepoVariant.OpenVINO:
                 result.add(path)
 
         elif "flax_model" in path.name:
-            if variant == ModelRepoVariant.FLAX:
+            if variant == ModelRepoVariant.Flax:
                 result.add(path)
 
         elif path.suffix in [".json", ".txt"]:
             result.add(path)
 
-        elif path.suffix in [".bin", ".safetensors", ".pt", ".ckpt"] and variant in [
+        elif variant in [
             ModelRepoVariant.FP16,
             ModelRepoVariant.FP32,
-            ModelRepoVariant.DEFAULT,
-        ]:
-            parent = path.parent
-            suffixes = path.suffixes
-            if len(suffixes) == 2:
-                variant_label, suffix = suffixes
-                basename = parent / Path(path.stem).stem
-            else:
-                variant_label = ""
-                suffix = suffixes[0]
-                basename = parent / path.stem
+            ModelRepoVariant.Default,
+        ] and path.suffix in [".bin", ".safetensors", ".pt", ".ckpt"]:
+            # For weights files, we want to select the best one for each subfolder. For example, we may have multiple
+            # text encoders:
+            #
+            # - text_encoder/model.fp16.safetensors
+            # - text_encoder/model.safetensors
+            # - text_encoder/pytorch_model.bin
+            # - text_encoder/pytorch_model.fp16.bin
+            #
+            # We prefer safetensors over other file formats and an exact variant match. We'll score each file based on
+            # variant and format and select the best one.
 
-            if previous := basenames.get(basename):
-                if (
-                    previous.suffix != ".safetensors" and suffix == ".safetensors"
-                ):  # replace non-safetensors with safetensors when available
-                    basenames[basename] = path
-                if variant_label == f".{variant}":
-                    basenames[basename] = path
-                elif not variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.DEFAULT]:
-                    basenames[basename] = path
-            else:
-                basenames[basename] = path
+            parent = path.parent
+            score = 0
+
+            if path.suffix == ".safetensors":
+                score += 1
+
+            candidate_variant_label = path.suffixes[0] if len(path.suffixes) == 2 else None
+
+            # Some special handling is needed here if there is not an exact match and if we cannot infer the variant
+            # from the file name. In this case, we only give this file a point if the requested variant is FP32 or DEFAULT.
+            if candidate_variant_label == f".{variant}" or (
+                not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]
+            ):
+                score += 1
+
+            if parent not in subfolder_weights:
+                subfolder_weights[parent] = []
+
+            subfolder_weights[parent].append(SubfolderCandidate(path=path, score=score))
 
         else:
             continue
 
-    for v in basenames.values():
-        result.add(v)
+    for candidate_list in subfolder_weights.values():
+        highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
+        if highest_score_candidate:
+            result.add(highest_score_candidate.path)
 
     # If one of the architecture-related variants was specified and no files matched other than
     # config and text files then we return an empty list
     if (
         variant
-        and variant in [ModelRepoVariant.ONNX, ModelRepoVariant.OPENVINO, ModelRepoVariant.FLAX]
+        and variant in [ModelRepoVariant.ONNX, ModelRepoVariant.OpenVINO, ModelRepoVariant.Flax]
         and not any(variant.value in x.name for x in result)
     ):
         return set()

invokeai/backend/util/test_utils.py

@@ -42,9 +42,10 @@ def install_and_load_model(
     # If the requested model is already installed, return its LoadedModel
     with contextlib.suppress(UnknownModelException):
         # TODO: Replace with wrapper call
-        loaded_model: LoadedModel = model_manager.load_model_by_attr(
+        configs = model_manager.store.search_by_attr(
             model_name=model_name, base_model=base_model, model_type=model_type
         )
+        loaded_model: LoadedModel = model_manager.load.load_model(configs[0])
         return loaded_model
 
     # Install the requested model.
@@ -53,7 +54,7 @@ def install_and_load_model(
     assert job.complete
 
     try:
-        loaded_model = model_manager.load_model_by_config(job.config_out)
+        loaded_model = model_manager.load.load_model(job.config_out)
         return loaded_model
     except UnknownModelException as e:
         raise Exception(

invokeai/frontend/install/model_install.py

@@ -144,7 +144,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
 
         self.nextrely = top_of_table
         self.lora_models = self.add_model_widgets(
-            model_type=ModelType.Lora,
+            model_type=ModelType.LoRA,
             window_width=window_width,
         )
         bottom_of_table = max(bottom_of_table, self.nextrely)

invokeai/frontend/merge/merge_diffusers.py

@@ -20,7 +20,6 @@ from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.download import DownloadQueueService
 from invokeai.app.services.image_files.image_files_disk import DiskImageFileStorage
 from invokeai.app.services.model_install import ModelInstallService
-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 (
@@ -413,7 +412,7 @@ def get_config_store() -> ModelRecordServiceSQL:
     assert output_path is not None
     image_files = DiskImageFileStorage(output_path / "images")
     db = init_db(config=config, logger=InvokeAILogger.get_logger(), image_files=image_files)
-    return ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
+    return ModelRecordServiceSQL(db)
 
 
 def get_model_merger(record_store: ModelRecordServiceBase) -> ModelMerger:

invokeai/frontend/web/package.json

@@ -30,7 +30,7 @@
     "lint:prettier": "prettier --check .",
     "lint:tsc": "tsc --noEmit",
     "lint": "concurrently -g -c red,green,yellow,blue,magenta pnpm:lint:*",
-    "fix": "knip --fix && eslint --fix . && prettier --log-level warn --write .",
+    "fix": "eslint --fix . && prettier --log-level warn --write .",
     "preinstall": "npx only-allow pnpm",
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",

invokeai/frontend/web/public/locales/en.json

@@ -304,6 +304,12 @@
             "method": "High Resolution Fix Method"
         }
     },
+    "prompt": {
+        "addPromptTrigger": "Add Prompt Trigger",
+        "compatibleEmbeddings": "Compatible Embeddings",
+        "noPromptTriggers": "No triggers available",
+        "noMatchingTriggers": "No matching triggers"
+    },
     "embedding": {
         "addEmbedding": "Add Embedding",
         "incompatibleModel": "Incompatible base model:",
@@ -740,6 +746,7 @@
         "delete": "Delete",
         "deleteConfig": "Delete Config",
         "deleteModel": "Delete Model",
+        "deleteModelImage": "Delete Model Image",
         "deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
         "deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
         "description": "Description",
@@ -780,6 +787,10 @@
         "modelDeleteFailed": "Failed to delete model",
         "modelEntryDeleted": "Model Entry Deleted",
         "modelExists": "Model Exists",
+        "modelImageDeleted": "Model Image Deleted",
+        "modelImageDeleteFailed": "Model Image Delete Failed",
+        "modelImageUpdated": "Model Image Updated",
+        "modelImageUpdateFailed": "Model Image Update Failed",
         "modelLocation": "Model Location",
         "modelLocationValidationMsg": "Provide the path to a local folder where your Diffusers Model is stored",
         "modelManager": "Model Manager",
@@ -812,6 +823,7 @@
         "oliveModels": "Olives",
         "onnxModels": "Onnx",
         "path": "Path",
+        "pathToConfig": "Path To Config",
         "pathToCustomConfig": "Path To Custom Config",
         "pickModelType": "Pick Model Type",
         "predictionType": "Prediction Type",
@@ -846,6 +858,7 @@
         "triggerPhrases": "Trigger Phrases",
         "typePhraseHere": "Type phrase here",
         "upcastAttention": "Upcast Attention",
+        "uploadImage": "Upload Image",
         "updateModel": "Update Model",
         "useCustomConfig": "Use Custom Config",
         "useDefaultSettings": "Use Default Settings",
@@ -938,6 +951,7 @@
         "doesNotExist": "does not exist",
         "downloadWorkflow": "Download Workflow JSON",
         "edge": "Edge",
+        "edit": "Edit",
         "editMode": "Edit in Workflow Editor",
         "enum": "Enum",
         "enumDescription": "Enums are values that may be one of a number of options.",
@@ -1013,6 +1027,7 @@
         "nodeTemplate": "Node Template",
         "nodeType": "Node Type",
         "noFieldsLinearview": "No fields added to Linear View",
+        "noFieldsViewMode": "This workflow has no selected fields to display. View the full workflow to configure values.",
         "noFieldType": "No field type",
         "noImageFoundState": "No initial image found in state",
         "noMatchingNodes": "No matching nodes",
@@ -1800,6 +1815,7 @@
         "cursorPosition": "Cursor Position",
         "darkenOutsideSelection": "Darken Outside Selection",
         "discardAll": "Discard All",
+        "discardCurrent": "Discard Current",
         "downloadAsImage": "Download As Image",
         "emptyFolder": "Empty Folder",
         "emptyTempImageFolder": "Empty Temp Image Folder",
@@ -1809,6 +1825,7 @@
         "eraseBoundingBox": "Erase Bounding Box",
         "eraser": "Eraser",
         "fillBoundingBox": "Fill Bounding Box",
+        "invertBrushSizeScrollDirection": "Invert Scroll for Brush Size",
         "layer": "Layer",
         "limitStrokesToBox": "Limit Strokes to Box",
         "mask": "Mask",

invokeai/frontend/web/public/locales/it.json

@@ -115,7 +115,8 @@
         "safetensors": "Safetensors",
         "ai": "ia",
         "file": "File",
-        "toResolve": "Da risolvere"
+        "toResolve": "Da risolvere",
+        "add": "Aggiungi"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -153,7 +154,12 @@
         "starImage": "Immagine preferita",
         "dropToUpload": "$t(gallery.drop) per aggiornare",
         "problemDeletingImagesDesc": "Impossibile eliminare una o più immagini",
-        "problemDeletingImages": "Problema durante l'eliminazione delle immagini"
+        "problemDeletingImages": "Problema durante l'eliminazione delle immagini",
+        "bulkDownloadRequested": "Preparazione del download",
+        "bulkDownloadRequestedDesc": "La tua richiesta di download è in preparazione. L'operazione potrebbe richiedere alcuni istanti.",
+        "bulkDownloadRequestFailed": "Problema durante la preparazione del download",
+        "bulkDownloadStarting": "Avvio scaricamento",
+        "bulkDownloadFailed": "Scaricamento fallito"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti di scelta rapida",
@@ -505,12 +511,12 @@
         "modelSyncFailed": "Sincronizzazione modello non riuscita",
         "settings": "Impostazioni",
         "syncModels": "Sincronizza Modelli",
-        "syncModelsDesc": "Se i tuoi modelli non sono sincronizzati con il back-end, puoi aggiornarli utilizzando questa opzione. Questo è generalmente utile nei casi in cui aggiorni manualmente il tuo file models.yaml o aggiungi modelli alla cartella principale di InvokeAI dopo l'avvio dell'applicazione.",
+        "syncModelsDesc": "Se i tuoi modelli non sono sincronizzati con il back-end, puoi aggiornarli utilizzando questa opzione. Questo è generalmente utile nei casi in cui aggiungi modelli alla cartella principale di InvokeAI dopo l'avvio dell'applicazione.",
         "loraModels": "LoRA",
         "oliveModels": "Olive",
         "onnxModels": "ONNX",
         "noModels": "Nessun modello trovato",
-        "predictionType": "Tipo di previsione (per modelli Stable Diffusion 2.x ed alcuni modelli Stable Diffusion 1.x)",
+        "predictionType": "Tipo di previsione",
         "quickAdd": "Aggiunta rapida",
         "simpleModelDesc": "Fornire un percorso a un modello diffusori locale, un modello checkpoint/safetensor locale, un ID repository HuggingFace o un URL del modello checkpoint/diffusori.",
         "advanced": "Avanzate",
@@ -521,7 +527,34 @@
         "vaePrecision": "Precisione VAE",
         "noModelSelected": "Nessun modello selezionato",
         "conversionNotSupported": "Conversione non supportata",
-        "configFile": "File di configurazione"
+        "configFile": "File di configurazione",
+        "modelName": "Nome del modello",
+        "modelSettings": "Impostazioni del modello",
+        "advancedImportInfo": "La scheda opzioni avanzate consente la configurazione manuale delle impostazioni del modello principale. Utilizza questa scheda solo se sei sicuro di conoscere il tipo di modello e la configurazione corretti per il modello selezionato.",
+        "addAll": "Aggiungi tutto",
+        "addModels": "Aggiungi modelli",
+        "cancel": "Annulla",
+        "edit": "Modifica",
+        "imageEncoderModelId": "ID modello codificatore di immagini",
+        "importQueue": "Coda di importazione",
+        "modelMetadata": "Metadati del modello",
+        "path": "Percorso",
+        "prune": "Elimina",
+        "pruneTooltip": "Elimina dalla coda le importazioni completate",
+        "removeFromQueue": "Rimuovi dalla coda",
+        "repoVariant": "Variante del repository",
+        "scan": "Scansiona",
+        "scanFolder": "Scansione cartella",
+        "scanResults": "Risultati della scansione",
+        "source": "Sorgente",
+        "upcastAttention": "Eleva l'attenzione",
+        "ztsnrTraining": "Addestramento ZTSNR",
+        "typePhraseHere": "Digita la frase qui",
+        "defaultSettingsSaved": "Impostazioni predefinite salvate",
+        "defaultSettings": "Impostazioni predefinite",
+        "metadata": "Metadati",
+        "useDefaultSettings": "Usa le impostazioni predefinite",
+        "triggerPhrases": "Frasi trigger"
     },
     "parameters": {
         "images": "Immagini",
@@ -603,8 +636,8 @@
         "clipSkip": "CLIP Skip",
         "aspectRatio": "Proporzioni",
         "maskAdjustmentsHeader": "Regolazioni della maschera",
-        "maskBlur": "Sfocatura",
-        "maskBlurMethod": "Metodo di sfocatura",
+        "maskBlur": "Sfocatura maschera",
+        "maskBlurMethod": "Metodo sfocatura maschera",
         "seamLowThreshold": "Basso",
         "seamHighThreshold": "Alto",
         "coherencePassHeader": "Passaggio di coerenza",
@@ -661,7 +694,8 @@
         "setToOptimalSizeTooLarge": "$t(parameters.setToOptimalSize) (potrebbe essere troppo grande)",
         "boxBlur": "Box",
         "gaussianBlur": "Gaussian",
-        "remixImage": "Remixa l'immagine"
+        "remixImage": "Remixa l'immagine",
+        "coherenceEdgeSize": "Dimensione bordo"
     },
     "settings": {
         "models": "Modelli",
@@ -744,8 +778,8 @@
         "canceled": "Elaborazione annullata",
         "problemCopyingImageLink": "Impossibile copiare il collegamento dell'immagine",
         "uploadFailedInvalidUploadDesc": "Deve essere una singola immagine PNG o JPEG",
-        "parameterSet": "Parametro impostato",
-        "parameterNotSet": "Parametro non impostato",
+        "parameterSet": "{{parameter}} impostato",
+        "parameterNotSet": "{{parameter}} non impostato",
         "nodesLoadedFailed": "Impossibile caricare i nodi",
         "nodesSaved": "Nodi salvati",
         "nodesLoaded": "Nodi caricati",
@@ -798,7 +832,10 @@
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
         "uploadInitialImage": "Carica l'immagine iniziale",
-        "problemDownloadingImage": "Impossibile scaricare l'immagine"
+        "problemDownloadingImage": "Impossibile scaricare l'immagine",
+        "prunedQueue": "Coda ripulita",
+        "modelImportCanceled": "Importazione del modello annullata",
+        "modelImportRemoved": "Importazione del modello rimossa"
     },
     "tooltip": {
         "feature": {
@@ -876,7 +913,10 @@
         "antialiasing": "Anti aliasing",
         "showResultsOn": "Mostra i risultati (attivato)",
         "showResultsOff": "Mostra i risultati (disattivato)",
-        "saveMask": "Salva $t(unifiedCanvas.mask)"
+        "saveMask": "Salva $t(unifiedCanvas.mask)",
+        "coherenceModeGaussianBlur": "Sfocatura Gaussiana",
+        "coherenceModeBoxBlur": "Sfocatura Box",
+        "coherenceModeStaged": "Maschera espansa"
     },
     "accessibility": {
         "modelSelect": "Seleziona modello",
@@ -1345,7 +1385,8 @@
         "allLoRAsAdded": "Tutti i LoRA aggiunti",
         "defaultVAE": "VAE predefinito",
         "incompatibleBaseModel": "Modello base incompatibile",
-        "loraAlreadyAdded": "LoRA già aggiunto"
+        "loraAlreadyAdded": "LoRA già aggiunto",
+        "concepts": "Concetti"
     },
     "invocationCache": {
         "disable": "Disabilita",
@@ -1698,6 +1739,25 @@
             "paragraphs": [
                 "Valuta le generazioni in modo che siano più simili alle immagini con un punteggio estetico elevato, in base ai dati di addestramento."
             ]
+        },
+        "compositingCoherenceMinDenoise": {
+            "heading": "Livello minimo di riduzione del rumore",
+            "paragraphs": [
+                "Intensità minima di riduzione rumore per la modalità di Coerenza",
+                "L'intensità minima di riduzione del rumore per la regione di coerenza durante l'inpainting o l'outpainting"
+            ]
+        },
+        "compositingMaskBlur": {
+            "paragraphs": [
+                "Il raggio di sfocatura della maschera."
+            ],
+            "heading": "Sfocatura maschera"
+        },
+        "compositingCoherenceEdgeSize": {
+            "heading": "Dimensione del bordo",
+            "paragraphs": [
+                "La dimensione del bordo del passaggio di coerenza."
+            ]
         }
     },
     "sdxl": {
@@ -1746,7 +1806,12 @@
         "scheduler": "Campionatore",
         "recallParameters": "Richiama i parametri",
         "noRecallParameters": "Nessun parametro da richiamare trovato",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "allPrompts": "Tutti i prompt",
+        "imageDimensions": "Dimensioni dell'immagine",
+        "parameterSet": "Parametro {{parameter}} impostato",
+        "parsingFailed": "Analisi non riuscita",
+        "recallParameter": "Richiama {{label}}"
     },
     "hrf": {
         "enableHrf": "Abilita Correzione Alta Risoluzione",
@@ -1818,5 +1883,11 @@
         "image": {
             "title": "Immagine"
         }
+    },
+    "prompt": {
+        "compatibleEmbeddings": "Incorporamenti compatibili",
+        "addPromptTrigger": "Aggiungi parola chiave nel prompt",
+        "noPromptTriggers": "Nessuna parola chiave disponibile",
+        "noMatchingTriggers": "Nessuna parola chiave corrispondente"
     }
 }

invokeai/frontend/web/public/locales/ru.json

@@ -52,7 +52,7 @@
         "accept": "Принять",
         "postprocessing": "Постобработка",
         "txt2img": "Текст в изображение (txt2img)",
-        "linear": "Линейная обработка",
+        "linear": "Линейный вид",
         "dontAskMeAgain": "Больше не спрашивать",
         "areYouSure": "Вы уверены?",
         "random": "Случайное",
@@ -117,7 +117,8 @@
         "toResolve": "Чтоб решить",
         "copy": "Копировать",
         "localSystem": "Локальная система",
-        "aboutDesc": "Используя Invoke для работы? Проверьте это:"
+        "aboutDesc": "Используя Invoke для работы? Проверьте это:",
+        "add": "Добавить"
     },
     "gallery": {
         "generations": "Генерации",
@@ -155,7 +156,12 @@
         "noImageSelected": "Изображение не выбрано",
         "setCurrentImage": "Установить как текущее изображение",
         "starImage": "Добавить в избранное",
-        "dropToUpload": "$t(gallery.drop) чтоб загрузить"
+        "dropToUpload": "$t(gallery.drop) чтоб загрузить",
+        "bulkDownloadFailed": "Загрузка не удалась",
+        "bulkDownloadStarting": "Начало загрузки",
+        "bulkDownloadRequested": "Подготовка к скачиванию",
+        "bulkDownloadRequestedDesc": "Ваш запрос на скачивание готовится. Это может занять несколько минут.",
+        "bulkDownloadRequestFailed": "Возникла проблема при подготовке скачивания"
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -504,7 +510,7 @@
         "settings": "Настройки",
         "selectModel": "Выберите модель",
         "syncModels": "Синхронизация моделей",
-        "syncModelsDesc": "Если ваши модели не синхронизированы с серверной частью, вы можете обновить их, используя эту опцию. Обычно это удобно в тех случаях, когда вы вручную обновляете свой файл \"models.yaml\" или добавляете модели в корневую папку InvokeAI после загрузки приложения.",
+        "syncModelsDesc": "Если ваши модели не синхронизированы с серверной частью, вы можете обновить их с помощью этой опции. Обычно это удобно в тех случаях, когда вы добавляете модели в корневую папку InvokeAI или каталог автоимпорта после загрузки приложения.",
         "modelUpdateFailed": "Не удалось обновить модель",
         "modelConversionFailed": "Не удалось сконвертировать модель",
         "modelsMergeFailed": "Не удалось выполнить слияние моделей",
@@ -513,7 +519,7 @@
         "oliveModels": "Модели Olives",
         "conversionNotSupported": "Преобразование не поддерживается",
         "noModels": "Нет моделей",
-        "predictionType": "Тип прогноза (для моделей Stable Diffusion 2.x и периодических моделей Stable Diffusion 1.x)",
+        "predictionType": "Тип прогноза",
         "quickAdd": "Быстрое добавление",
         "simpleModelDesc": "Укажите путь к локальной модели Diffusers , локальной модели checkpoint / safetensors, идентификатор репозитория HuggingFace или URL-адрес модели контрольной checkpoint / diffusers.",
         "advanced": "Продвинутый",
@@ -524,7 +530,33 @@
         "customConfigFileLocation": "Расположение пользовательского файла конфигурации",
         "vaePrecision": "Точность VAE",
         "noModelSelected": "Модель не выбрана",
-        "configFile": "Файл конфигурации"
+        "configFile": "Файл конфигурации",
+        "addAll": "Добавить всё",
+        "addModels": "Добавить модели",
+        "cancel": "Отмена",
+        "defaultSettings": "Стандартные настройки",
+        "importQueue": "Импортировать очередь",
+        "metadata": "Метаданные",
+        "imageEncoderModelId": "ID модели-энкодера изображений",
+        "typePhraseHere": "Введите фразы здесь",
+        "advancedImportInfo": "Вкладка «Дополнительно» позволяет вручную настроить основные параметры модели. Используйте эту вкладку только в том случае, если вы уверены, что знаете правильный тип модели и конфигурацию выбранной модели.",
+        "defaultSettingsSaved": "Стандартные настройки сохранены",
+        "edit": "Редактировать",
+        "path": "Путь",
+        "prune": "Удалить",
+        "pruneTooltip": "Удалить готовые импорты из очереди",
+        "removeFromQueue": "Удалить из очереди",
+        "repoVariant": "Вариант репозитория",
+        "scan": "Сканировать",
+        "scanFolder": "Сканировать папку",
+        "scanResults": "Результаты сканирования",
+        "source": "Источник",
+        "triggerPhrases": "Триггерные фразы",
+        "useDefaultSettings": "Использовать стандартные настройки",
+        "modelMetadata": "Метаданные модели",
+        "modelName": "Название модели",
+        "modelSettings": "Настройки модели",
+        "upcastAttention": "Внимание"
     },
     "parameters": {
         "images": "Изображения",
@@ -591,7 +623,7 @@
         "hSymmetryStep": "Шаг гор. симметрии",
         "hidePreview": "Скрыть предпросмотр",
         "imageToImage": "Изображение в изображение",
-        "denoisingStrength": "Сила шумоподавления",
+        "denoisingStrength": "Сила зашумления",
         "copyImage": "Скопировать изображение",
         "showPreview": "Показать предпросмотр",
         "noiseSettings": "Шум",
@@ -606,8 +638,8 @@
         "clipSkip": "CLIP Пропуск",
         "aspectRatio": "Соотношение",
         "maskAdjustmentsHeader": "Настройка маски",
-        "maskBlur": "Размытие",
-        "maskBlurMethod": "Метод размытия",
+        "maskBlur": "Размытие маски",
+        "maskBlurMethod": "Метод размытия маски",
         "seamLowThreshold": "Низкий",
         "seamHighThreshold": "Высокий",
         "coherencePassHeader": "Порог Coherence",
@@ -666,7 +698,9 @@
         "lockAspectRatio": "Заблокировать соотношение",
         "boxBlur": "Размытие прямоугольника",
         "gaussianBlur": "Размытие по Гауссу",
-        "remixImage": "Ремикс изображения"
+        "remixImage": "Ремикс изображения",
+        "coherenceMinDenoise": "Мин. шумоподавление",
+        "coherenceEdgeSize": "Размер края"
     },
     "settings": {
         "models": "Модели",
@@ -749,8 +783,8 @@
         "canceled": "Обработка отменена",
         "problemCopyingImageLink": "Не удалось скопировать ссылку на изображение",
         "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
-        "parameterNotSet": "Параметр не задан",
-        "parameterSet": "Параметр задан",
+        "parameterNotSet": "Параметр {{parameter}} не задан",
+        "parameterSet": "Параметр {{parameter}} задан",
         "nodesLoaded": "Узлы загружены",
         "problemCopyingImage": "Не удается скопировать изображение",
         "nodesLoadedFailed": "Не удалось загрузить Узлы",
@@ -803,7 +837,10 @@
         "problemImportingMask": "Проблема с импортом маски",
         "problemDownloadingImage": "Не удается скачать изображение",
         "uploadInitialImage": "Загрузить начальное изображение",
-        "resetInitialImage": "Сбросить начальное изображение"
+        "resetInitialImage": "Сбросить начальное изображение",
+        "prunedQueue": "Урезанная очередь",
+        "modelImportCanceled": "Импорт модели отменен",
+        "modelImportRemoved": "Импорт модели удален"
     },
     "tooltip": {
         "feature": {
@@ -1145,7 +1182,11 @@
         "reorderLinearView": "Изменить порядок линейного просмотра",
         "viewMode": "Использовать в линейном представлении",
         "editMode": "Открыть в редакторе узлов",
-        "resetToDefaultValue": "Сбросить к стандартному значкнию"
+        "resetToDefaultValue": "Сбросить к стандартному значкнию",
+        "latentsField": "Латенты",
+        "latentsCollectionDescription": "Латенты могут передаваться между узлами.",
+        "latentsPolymorphicDescription": "Латенты могут передаваться между узлами.",
+        "latentsFieldDescription": "Латенты могут передаваться между узлами."
     },
     "controlnet": {
         "amult": "a_mult",
@@ -1294,7 +1335,8 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "Планировщик определяет, как итеративно добавлять шум к изображению или как обновлять образец на основе выходных данных модели."
+                "Планировщик, используемый в процессе генерации.",
+                "Каждый планировщик определяет, как итеративно добавлять шум к изображению или как обновлять образец на основе выходных данных модели."
             ],
             "heading": "Планировщик"
         },
@@ -1347,7 +1389,7 @@
         "compositingCoherenceMode": {
             "heading": "Режим",
             "paragraphs": [
-                "Режим прохождения когерентности."
+                "Метод, используемый для создания связного изображения с вновь созданной замаскированной областью."
             ]
         },
         "paramSeed": {
@@ -1365,7 +1407,7 @@
         },
         "controlNetBeginEnd": {
             "paragraphs": [
-                "На каких этапах процесса шумоподавления будет применена ControlNet.",
+                "Часть процесса шумоподавления, к которой будет применен адаптер контроля.",
                 "ControlNet, применяемые в начале процесса, направляют композицию, а ControlNet, применяемые в конце, направляют детали."
             ],
             "heading": "Процент начала/конца шага"
@@ -1381,8 +1423,8 @@
         },
         "clipSkip": {
             "paragraphs": [
-                "Выберите, сколько слоев модели CLIP нужно пропустить.",
-                "Некоторые модели работают лучше с определенными настройками пропуска CLIP."
+                "Сколько слоев модели CLIP пропустить.",
+                "Некоторые модели лучше подходят для использования с CLIP Skip."
             ],
             "heading": "CLIP пропуск"
         },
@@ -1479,6 +1521,25 @@
             "paragraphs": [
                 "Более высокий вес LoRA приведет к большему влиянию на конечное изображение."
             ]
+        },
+        "compositingMaskBlur": {
+            "heading": "Размытие маски",
+            "paragraphs": [
+                "Радиус размытия маски."
+            ]
+        },
+        "compositingCoherenceMinDenoise": {
+            "heading": "Минимальное шумоподавление",
+            "paragraphs": [
+                "Минимальный уровень шумоподавления для режима Coherence",
+                "Минимальный уровень шумоподавления для области когерентности при перерисовывании или дорисовке"
+            ]
+        },
+        "compositingCoherenceEdgeSize": {
+            "heading": "Размер края",
+            "paragraphs": [
+                "Размер края прохода когерентности."
+            ]
         }
     },
     "metadata": {
@@ -1509,7 +1570,12 @@
         "steps": "Шаги",
         "scheduler": "Планировщик",
         "noRecallParameters": "Параметры для вызова не найдены",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "parameterSet": "Параметр {{parameter}} установлен",
+        "parsingFailed": "Не удалось выполнить синтаксический анализ",
+        "recallParameter": "Отозвать {{label}}",
+        "allPrompts": "Все запросы",
+        "imageDimensions": "Размеры изображения"
     },
     "queue": {
         "status": "Статус",
@@ -1588,10 +1654,11 @@
         "denoisingStrength": "Шумоподавление",
         "refinermodel": "Модель перерисовщик",
         "posAestheticScore": "Положительная эстетическая оценка",
-        "concatPromptStyle": "Объединение запроса и стиля",
+        "concatPromptStyle": "Связывание запроса и стиля",
         "loading": "Загрузка...",
         "steps": "Шаги",
-        "posStylePrompt": "Запрос стиля"
+        "posStylePrompt": "Запрос стиля",
+        "freePromptStyle": "Ручной запрос стиля"
     },
     "invocationCache": {
         "useCache": "Использовать кэш",
@@ -1678,7 +1745,8 @@
         "allLoRAsAdded": "Все LoRA добавлены",
         "defaultVAE": "Стандартное VAE",
         "incompatibleBaseModel": "Несовместимая базовая модель",
-        "loraAlreadyAdded": "LoRA уже добавлена"
+        "loraAlreadyAdded": "LoRA уже добавлена",
+        "concepts": "Концепты"
     },
     "app": {
         "storeNotInitialized": "Магазин не инициализирован"
@@ -1696,7 +1764,7 @@
         },
         "generation": {
             "title": "Генерация",
-            "conceptsTab": "Концепты",
+            "conceptsTab": "LoRA",
             "modelTab": "Модель"
         },
         "advanced": {

invokeai/frontend/web/scripts/typegen.js

@@ -5,18 +5,55 @@ import openapiTS from 'openapi-typescript';
 const OPENAPI_URL = 'http://127.0.0.1:9090/openapi.json';
 const OUTPUT_FILE = 'src/services/api/schema.ts';
 
-async function main() {
-  process.stdout.write(`Generating types "${OPENAPI_URL}" --> "${OUTPUT_FILE}"...`);
-  const types = await openapiTS(OPENAPI_URL, {
+async function generateTypes(schema) {
+  process.stdout.write(`Generating types ${OUTPUT_FILE}...`);
+  const types = await openapiTS(schema, {
     exportType: true,
     transform: (schemaObject) => {
       if ('format' in schemaObject && schemaObject.format === 'binary') {
         return schemaObject.nullable ? 'Blob | null' : 'Blob';
       }
+      if (schemaObject.title === 'MetadataField') {
+        // This is `Record<string, never>` by default, but it actually accepts any a dict of any valid JSON value.
+        return 'Record<string, unknown>';
+      }
     },
   });
   fs.writeFileSync(OUTPUT_FILE, types);
   process.stdout.write(`\nOK!\r\n`);
 }
 
+async function main() {
+  const encoding = 'utf-8';
+
+  if (process.stdin.isTTY) {
+    // Handle generating types with an arg (e.g. URL or path to file)
+    if (process.argv.length > 3) {
+      console.error('Usage: typegen.js <openapi.json>');
+      process.exit(1);
+    }
+    if (process.argv[2]) {
+      const schema = new Buffer.from(process.argv[2], encoding);
+      generateTypes(schema);
+    } else {
+      generateTypes(OPENAPI_URL);
+    }
+  } else {
+    // Handle generating types from stdin
+    let schema = '';
+    process.stdin.setEncoding(encoding);
+
+    process.stdin.on('readable', function () {
+      const chunk = process.stdin.read();
+      if (chunk !== null) {
+        schema += chunk;
+      }
+    });
+
+    process.stdin.on('end', function () {
+      generateTypes(JSON.parse(schema));
+    });
+  }
+}
+
 main();

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -153,7 +153,7 @@ addFirstListImagesListener(startAppListening);
 // Ad-hoc upscale workflwo
 addUpscaleRequestedListener(startAppListening);
 
-// Dynamic prompts
+// Prompts
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasImageToControlNet.ts

@@ -38,7 +38,7 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
             type: 'image/png',
           }),
           image_category: 'control',
-          is_intermediate: false,
+          is_intermediate: true,
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
           crop_visible: false,
           postUploadAction: {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet.ts

@@ -48,7 +48,7 @@ export const addCanvasMaskToControlNetListener = (startAppListening: AppStartLis
             type: 'image/png',
           }),
           image_category: 'mask',
-          is_intermediate: false,
+          is_intermediate: true,
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
           crop_visible: false,
           postUploadAction: {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas.ts

@@ -101,7 +101,7 @@ export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartLis
         ).unwrap();
       }
 
-      const graph = buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
+      const graph = await buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
 
       log.debug({ graph: parseify(graph) }, `Canvas graph built`);
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear.ts

@@ -20,15 +20,15 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
 
       if (model && model.base === 'sdxl') {
         if (action.payload.tabName === 'txt2img') {
-          graph = buildLinearSDXLTextToImageGraph(state);
+          graph = await buildLinearSDXLTextToImageGraph(state);
         } else {
-          graph = buildLinearSDXLImageToImageGraph(state);
+          graph = await buildLinearSDXLImageToImageGraph(state);
         }
       } else {
         if (action.payload.tabName === 'txt2img') {
-          graph = buildLinearTextToImageGraph(state);
+          graph = await buildLinearTextToImageGraph(state);
         } else {
-          graph = buildLinearImageToImageGraph(state);
+          graph = await buildLinearImageToImageGraph(state);
         }
       }
 

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts

@@ -7,8 +7,10 @@ import {
   selectAllT2IAdapters,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { loraRemoved } from 'features/lora/store/loraSlice';
-import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
+import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
+import { heightChanged, modelChanged, vaeSelected, widthChanged } from 'features/parameters/store/generationSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
+import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import { refinerModelChanged } from 'features/sdxl/store/sdxlSlice';
 import { forEach, some } from 'lodash-es';
 import { mainModelsAdapterSelectors, modelsApi, vaeModelsAdapterSelectors } from 'services/api/endpoints/models';
@@ -24,7 +26,9 @@ export const addModelsLoadedListener = (startAppListening: AppStartListening) =>
       const log = logger('models');
       log.info({ models: action.payload.entities }, `Main models loaded (${action.payload.ids.length})`);
 
-      const currentModel = getState().generation.model;
+      const state = getState();
+
+      const currentModel = state.generation.model;
       const models = mainModelsAdapterSelectors.selectAll(action.payload);
 
       if (models.length === 0) {
@@ -39,6 +43,29 @@ export const addModelsLoadedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
+      const defaultModel = state.config.sd.defaultModel;
+      const defaultModelInList = defaultModel ? models.find((m) => m.key === defaultModel) : false;
+
+      if (defaultModelInList) {
+        const result = zParameterModel.safeParse(defaultModelInList);
+        if (result.success) {
+          dispatch(modelChanged(defaultModelInList, currentModel));
+
+          const optimalDimension = getOptimalDimension(defaultModelInList);
+          if (getIsSizeOptimal(state.generation.width, state.generation.height, optimalDimension)) {
+            return;
+          }
+          const { width, height } = calculateNewSize(
+            state.generation.aspectRatio.value,
+            optimalDimension * optimalDimension
+          );
+
+          dispatch(widthChanged(width));
+          dispatch(heightChanged(height));
+          return;
+        }
+      }
+
       const result = zParameterModel.safeParse(models[0]);
 
       if (!result.success) {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/setDefaultSettings.ts

@@ -34,13 +34,13 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
         return;
       }
 
-      const metadata = await dispatch(modelsApi.endpoints.getModelMetadata.initiate(currentModel.key)).unwrap();
+      const modelConfig = await dispatch(modelsApi.endpoints.getModelConfig.initiate(currentModel.key)).unwrap();
 
-      if (!metadata || !metadata.default_settings) {
+      if (!modelConfig || !modelConfig.default_settings) {
         return;
       }
 
-      const { vae, vae_precision, cfg_scale, cfg_rescale_multiplier, steps, scheduler } = metadata.default_settings;
+      const { vae, vae_precision, cfg_scale, cfg_rescale_multiplier, steps, scheduler } = modelConfig.default_settings;
 
       if (vae) {
         // we store this as "default" within default settings

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketModelInstall.ts

@@ -14,7 +14,7 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
       const { bytes, total_bytes, id } = action.payload.data;
 
       dispatch(
-        modelsApi.util.updateQueryData('getModelImports', undefined, (draft) => {
+        modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
           const modelImport = draft.find((m) => m.id === id);
           if (modelImport) {
             modelImport.bytes = bytes;
@@ -33,7 +33,7 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
       const { id } = action.payload.data;
 
       dispatch(
-        modelsApi.util.updateQueryData('getModelImports', undefined, (draft) => {
+        modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
           const modelImport = draft.find((m) => m.id === id);
           if (modelImport) {
             modelImport.status = 'completed';
@@ -41,7 +41,7 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
           return draft;
         })
       );
-      dispatch(api.util.invalidateTags([{ type: 'ModelConfig' }]));
+      dispatch(api.util.invalidateTags(['Model']));
     },
   });
 
@@ -51,7 +51,7 @@ export const addModelInstallEventListener = (startAppListening: AppStartListenin
       const { id, error, error_type } = action.payload.data;
 
       dispatch(
-        modelsApi.util.updateQueryData('getModelImports', undefined, (draft) => {
+        modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
           const modelImport = draft.find((m) => m.id === id);
           if (modelImport) {
             modelImport.status = 'error';

invokeai/frontend/web/src/common/components/IAIColorPicker.tsx

@@ -20,7 +20,7 @@ const sx: ChakraProps['sx'] = {
   '.react-colorful__hue-pointer': colorPickerPointerStyles,
   '.react-colorful__saturation-pointer': colorPickerPointerStyles,
   '.react-colorful__alpha-pointer': colorPickerPointerStyles,
-  gap: 2,
+  gap: 5,
   flexDir: 'column',
 };
 
@@ -39,8 +39,8 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
     <Flex sx={sx}>
       <RgbaColorPicker color={color} onChange={onChange} style={colorPickerStyles} {...rest} />
       {withNumberInput && (
-        <Flex>
-          <FormControl>
+        <Flex gap={5}>
+          <FormControl gap={0}>
             <FormLabel>{t('common.red')}</FormLabel>
             <CompositeNumberInput
               value={color.r}
@@ -52,7 +52,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={90}
             />
           </FormControl>
-          <FormControl>
+          <FormControl gap={0}>
             <FormLabel>{t('common.green')}</FormLabel>
             <CompositeNumberInput
               value={color.g}
@@ -64,7 +64,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={90}
             />
           </FormControl>
-          <FormControl>
+          <FormControl gap={0}>
             <FormLabel>{t('common.blue')}</FormLabel>
             <CompositeNumberInput
               value={color.b}
@@ -76,7 +76,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={255}
             />
           </FormControl>
-          <FormControl>
+          <FormControl gap={0}>
             <FormLabel>{t('common.alpha')}</FormLabel>
             <CompositeNumberInput
               value={color.a}

invokeai/frontend/web/src/features/canvas/components/IAICanvas.tsx

@@ -29,7 +29,7 @@ import { Layer, Stage } from 'react-konva';
 import IAICanvasBoundingBoxOverlay from './IAICanvasBoundingBoxOverlay';
 import IAICanvasGrid from './IAICanvasGrid';
 import IAICanvasIntermediateImage from './IAICanvasIntermediateImage';
-import IAICanvasMaskCompositer from './IAICanvasMaskCompositer';
+import IAICanvasMaskCompositor from './IAICanvasMaskCompositor';
 import IAICanvasMaskLines from './IAICanvasMaskLines';
 import IAICanvasObjectRenderer from './IAICanvasObjectRenderer';
 import IAICanvasStagingArea from './IAICanvasStagingArea';
@@ -176,7 +176,7 @@ const IAICanvas = () => {
           </Layer>
           <Layer id="mask" visible={isMaskEnabled && !isStaging} listening={false}>
             <IAICanvasMaskLines visible={true} listening={false} />
-            <IAICanvasMaskCompositer listening={false} />
+            <IAICanvasMaskCompositor listening={false} />
           </Layer>
           <Layer listening={false}>
             <IAICanvasBoundingBoxOverlay />

invokeai/frontend/web/src/features/canvas/components/IAICanvasMaskCompositor.tsx

@@ -16,9 +16,9 @@ const canvasMaskCompositerSelector = createMemoizedSelector(selectCanvasSlice, (
   };
 });
 
-type IAICanvasMaskCompositerProps = RectConfig;
+type IAICanvasMaskCompositorProps = RectConfig;
 
-const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
+const IAICanvasMaskCompositor = (props: IAICanvasMaskCompositorProps) => {
   const { ...rest } = props;
 
   const { stageCoordinates, stageDimensions } = useAppSelector(canvasMaskCompositerSelector);
@@ -89,4 +89,4 @@ const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
   );
 };
 
-export default memo(IAICanvasMaskCompositer);
+export default memo(IAICanvasMaskCompositor);

invokeai/frontend/web/src/features/canvas/components/IAICanvasStagingAreaToolbar.tsx

@@ -5,6 +5,7 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { stagingAreaImageSaved } from 'features/canvas/store/actions';
 import {
   commitStagingAreaImage,
+  discardStagedImage,
   discardStagedImages,
   nextStagingAreaImage,
   prevStagingAreaImage,
@@ -22,6 +23,7 @@ import {
   PiEyeBold,
   PiEyeSlashBold,
   PiFloppyDiskBold,
+  PiTrashSimpleBold,
   PiXBold,
 } from 'react-icons/pi';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
@@ -44,6 +46,40 @@ const selector = createMemoizedSelector(selectCanvasSlice, (canvas) => {
   };
 });
 
+const ClearStagingIntermediatesIconButton = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleDiscardStagingArea = useCallback(() => {
+    dispatch(discardStagedImages());
+  }, [dispatch]);
+
+  const handleDiscardStagingImage = useCallback(() => {
+    dispatch(discardStagedImage());
+  }, [dispatch]);
+
+  return (
+    <>
+      <IconButton
+        tooltip={`${t('unifiedCanvas.discardCurrent')}`}
+        aria-label={t('unifiedCanvas.discardCurrent')}
+        icon={<PiXBold />}
+        onClick={handleDiscardStagingImage}
+        colorScheme="invokeBlue"
+        fontSize={16}
+      />
+      <IconButton
+        tooltip={`${t('unifiedCanvas.discardAll')} (Esc)`}
+        aria-label={t('unifiedCanvas.discardAll')}
+        icon={<PiTrashSimpleBold />}
+        onClick={handleDiscardStagingArea}
+        colorScheme="error"
+        fontSize={16}
+      />
+    </>
+  );
+};
+
 const IAICanvasStagingAreaToolbar = () => {
   const dispatch = useAppDispatch();
   const { currentStagingAreaImage, shouldShowStagingImage, currentIndex, total } = useAppSelector(selector);
@@ -185,14 +221,7 @@ const IAICanvasStagingAreaToolbar = () => {
           onClick={handleSaveToGallery}
           colorScheme="invokeBlue"
         />
-        <IconButton
-          tooltip={`${t('unifiedCanvas.discardAll')} (Esc)`}
-          aria-label={t('unifiedCanvas.discardAll')}
-          icon={<PiXBold />}
-          onClick={handleDiscardStagingArea}
-          colorScheme="error"
-          fontSize={20}
-        />
+        <ClearStagingIntermediatesIconButton />
       </ButtonGroup>
     </Flex>
   );

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasSettingsButtonPopover.tsx

@@ -18,6 +18,7 @@ import {
   setShouldAutoSave,
   setShouldCropToBoundingBoxOnSave,
   setShouldDarkenOutsideBoundingBox,
+  setShouldInvertBrushSizeScrollDirection,
   setShouldRestrictStrokesToBox,
   setShouldShowCanvasDebugInfo,
   setShouldShowGrid,
@@ -40,6 +41,7 @@ const IAICanvasSettingsButtonPopover = () => {
   const shouldAutoSave = useAppSelector((s) => s.canvas.shouldAutoSave);
   const shouldCropToBoundingBoxOnSave = useAppSelector((s) => s.canvas.shouldCropToBoundingBoxOnSave);
   const shouldDarkenOutsideBoundingBox = useAppSelector((s) => s.canvas.shouldDarkenOutsideBoundingBox);
+  const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
   const shouldShowCanvasDebugInfo = useAppSelector((s) => s.canvas.shouldShowCanvasDebugInfo);
   const shouldShowGrid = useAppSelector((s) => s.canvas.shouldShowGrid);
   const shouldShowIntermediates = useAppSelector((s) => s.canvas.shouldShowIntermediates);
@@ -76,6 +78,10 @@ const IAICanvasSettingsButtonPopover = () => {
     (e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldDarkenOutsideBoundingBox(e.target.checked)),
     [dispatch]
   );
+  const handleChangeShouldInvertBrushSizeScrollDirection = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldInvertBrushSizeScrollDirection(e.target.checked)),
+    [dispatch]
+  );
   const handleChangeShouldAutoSave = useCallback(
     (e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldAutoSave(e.target.checked)),
     [dispatch]
@@ -144,6 +150,13 @@ const IAICanvasSettingsButtonPopover = () => {
                 <FormLabel>{t('unifiedCanvas.limitStrokesToBox')}</FormLabel>
                 <Checkbox isChecked={shouldRestrictStrokesToBox} onChange={handleChangeShouldRestrictStrokesToBox} />
               </FormControl>
+              <FormControl>
+                <FormLabel>{t('unifiedCanvas.invertBrushSizeScrollDirection')}</FormLabel>
+                <Checkbox
+                  isChecked={shouldInvertBrushSizeScrollDirection}
+                  onChange={handleChangeShouldInvertBrushSizeScrollDirection}
+                />
+              </FormControl>
               <FormControl>
                 <FormLabel>{t('unifiedCanvas.showCanvasDebugInfo')}</FormLabel>
                 <Checkbox isChecked={shouldShowCanvasDebugInfo} onChange={handleChangeShouldShowCanvasDebugInfo} />

invokeai/frontend/web/src/features/canvas/hooks/useCanvasZoom.ts

@@ -15,6 +15,7 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
   const stageScale = useAppSelector((s) => s.canvas.stageScale);
   const isMoveStageKeyHeld = useStore($isMoveStageKeyHeld);
   const brushSize = useAppSelector((s) => s.canvas.brushSize);
+  const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
 
   return useCallback(
     (e: KonvaEventObject<WheelEvent>) => {
@@ -28,10 +29,16 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
       // checking for ctrl key is pressed or not,
       // so that brush size can be controlled using ctrl + scroll up/down
 
+      // Invert the delta if the property is set to true
+      let delta = e.evt.deltaY;
+      if (shouldInvertBrushSizeScrollDirection) {
+        delta = -delta;
+      }
+
       if ($ctrl.get() || $meta.get()) {
         // This equation was derived by fitting a curve to the desired brush sizes and deltas
         // see https://github.com/invoke-ai/InvokeAI/pull/5542#issuecomment-1915847565
-        const targetDelta = Math.sign(e.evt.deltaY) * 0.7363 * Math.pow(1.0394, brushSize);
+        const targetDelta = Math.sign(delta) * 0.7363 * Math.pow(1.0394, brushSize);
         // This needs to be clamped to prevent the delta from getting too large
         const finalDelta = clamp(targetDelta, -20, 20);
         // The new brush size is also clamped to prevent it from getting too large or small
@@ -67,7 +74,7 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
         dispatch(setStageCoordinates(newCoordinates));
       }
     },
-    [stageRef, isMoveStageKeyHeld, stageScale, dispatch, brushSize]
+    [stageRef, isMoveStageKeyHeld, brushSize, dispatch, stageScale, shouldInvertBrushSizeScrollDirection]
   );
 };
 

invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts

@@ -65,6 +65,7 @@ const initialCanvasState: CanvasState = {
   shouldAutoSave: false,
   shouldCropToBoundingBoxOnSave: false,
   shouldDarkenOutsideBoundingBox: false,
+  shouldInvertBrushSizeScrollDirection: false,
   shouldLockBoundingBox: false,
   shouldPreserveMaskedArea: false,
   shouldRestrictStrokesToBox: true,
@@ -220,6 +221,9 @@ export const canvasSlice = createSlice({
     setShouldDarkenOutsideBoundingBox: (state, action: PayloadAction<boolean>) => {
       state.shouldDarkenOutsideBoundingBox = action.payload;
     },
+    setShouldInvertBrushSizeScrollDirection: (state, action: PayloadAction<boolean>) => {
+      state.shouldInvertBrushSizeScrollDirection = action.payload;
+    },
     clearCanvasHistory: (state) => {
       state.pastLayerStates = [];
       state.futureLayerStates = [];
@@ -288,6 +292,31 @@ export const canvasSlice = createSlice({
       state.shouldShowStagingImage = true;
       state.batchIds = [];
     },
+    discardStagedImage: (state) => {
+      const { images, selectedImageIndex } = state.layerState.stagingArea;
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
+
+      if (!images.length) {
+        return;
+      }
+
+      images.splice(selectedImageIndex, 1);
+
+      if (selectedImageIndex >= images.length) {
+        state.layerState.stagingArea.selectedImageIndex = images.length - 1;
+      }
+
+      if (!images.length) {
+        state.shouldShowStagingImage = false;
+        state.shouldShowStagingOutline = false;
+      }
+
+      state.futureLayerStates = [];
+    },
     addFillRect: (state) => {
       const { boundingBoxCoordinates, boundingBoxDimensions, brushColor } = state;
 
@@ -655,6 +684,7 @@ export const {
   commitColorPickerColor,
   commitStagingAreaImage,
   discardStagedImages,
+  discardStagedImage,
   nextStagingAreaImage,
   prevStagingAreaImage,
   redo,
@@ -674,6 +704,7 @@ export const {
   setShouldAutoSave,
   setShouldCropToBoundingBoxOnSave,
   setShouldDarkenOutsideBoundingBox,
+  setShouldInvertBrushSizeScrollDirection,
   setShouldPreserveMaskedArea,
   setShouldShowBoundingBox,
   setShouldShowCanvasDebugInfo,

invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts

@@ -120,6 +120,7 @@ export interface CanvasState {
   shouldAutoSave: boolean;
   shouldCropToBoundingBoxOnSave: boolean;
   shouldDarkenOutsideBoundingBox: boolean;
+  shouldInvertBrushSizeScrollDirection: boolean;
   shouldLockBoundingBox: boolean;
   shouldPreserveMaskedArea: boolean;
   shouldRestrictStrokesToBox: boolean;

invokeai/frontend/web/src/features/embedding/EmbeddingSelect.stories.tsx

@@ -1,21 +0,0 @@
-import type { Meta, StoryObj } from '@storybook/react';
-
-import { EmbeddingSelect } from './EmbeddingSelect';
-import type { EmbeddingSelectProps } from './types';
-
-const meta: Meta<typeof EmbeddingSelect> = {
-  title: 'Feature/Prompt/EmbeddingSelect',
-  tags: ['autodocs'],
-  component: EmbeddingSelect,
-};
-
-export default meta;
-type Story = StoryObj<typeof EmbeddingSelect>;
-
-const Component = (props: EmbeddingSelectProps) => {
-  return <EmbeddingSelect {...props}>Invoke</EmbeddingSelect>;
-};
-
-export const Default: Story = {
-  render: Component,
-};

invokeai/frontend/web/src/features/embedding/EmbeddingSelect.tsx

@@ -1,67 +0,0 @@
-import type { ChakraProps } from '@invoke-ai/ui-library';
-import { Combobox, FormControl } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
-import type { EmbeddingSelectProps } from 'features/embedding/types';
-import { t } from 'i18next';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useGetTextualInversionModelsQuery } from 'services/api/endpoints/models';
-import type { TextualInversionModelConfig } from 'services/api/types';
-
-const noOptionsMessage = () => t('embedding.noMatchingEmbedding');
-
-export const EmbeddingSelect = memo(({ onSelect, onClose }: EmbeddingSelectProps) => {
-  const { t } = useTranslation();
-
-  const currentBaseModel = useAppSelector((s) => s.generation.model?.base);
-
-  const getIsDisabled = useCallback(
-    (embedding: TextualInversionModelConfig): boolean => {
-      const isCompatible = currentBaseModel === embedding.base;
-      const hasMainModel = Boolean(currentBaseModel);
-      return !hasMainModel || !isCompatible;
-    },
-    [currentBaseModel]
-  );
-  const { data, isLoading } = useGetTextualInversionModelsQuery();
-
-  const _onChange = useCallback(
-    (embedding: TextualInversionModelConfig | null) => {
-      if (!embedding) {
-        return;
-      }
-      onSelect(embedding.name);
-    },
-    [onSelect]
-  );
-
-  const { options, onChange } = useGroupedModelCombobox({
-    modelEntities: data,
-    getIsDisabled,
-    onChange: _onChange,
-  });
-
-  return (
-    <FormControl>
-      <Combobox
-        placeholder={isLoading ? t('common.loading') : t('embedding.addEmbedding')}
-        defaultMenuIsOpen
-        autoFocus
-        value={null}
-        options={options}
-        noOptionsMessage={noOptionsMessage}
-        onChange={onChange}
-        onMenuClose={onClose}
-        data-testid="add-embedding"
-        sx={selectStyles}
-      />
-    </FormControl>
-  );
-});
-
-EmbeddingSelect.displayName = 'EmbeddingSelect';
-
-const selectStyles: ChakraProps['sx'] = {
-  w: 'full',
-};

invokeai/frontend/web/src/features/gallery/components/Boards/AutoAddIcon.tsx

@@ -6,7 +6,7 @@ const AutoAddIcon = () => {
   const { t } = useTranslation();
   return (
     <Flex position="absolute" insetInlineEnd={0} top={0} p={1}>
-      <Badge variant="solid" bg="invokeBlue.500">
+      <Badge variant="solid" bg="invokeBlue.400">
         {t('common.auto')}
       </Badge>
     </Flex>

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/GalleryBoard.tsx

@@ -173,8 +173,8 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
                   w="full"
                   maxW="full"
                   borderBottomRadius="base"
-                  bg={isSelected ? 'invokeBlue.500' : 'base.600'}
-                  color={isSelected ? 'base.50' : 'base.100'}
+                  bg={isSelected ? 'invokeBlue.400' : 'base.600'}
+                  color={isSelected ? 'base.800' : 'base.100'}
                   lineHeight="short"
                   fontSize="xs"
                 >
@@ -193,6 +193,7 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
                       overflow="hidden"
                       textOverflow="ellipsis"
                       noOfLines={1}
+                      color="inherit"
                     />
                     <EditableInput sx={editableInputStyles} />
                   </Editable>

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx

@@ -109,8 +109,8 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
                   w="full"
                   maxW="full"
                   borderBottomRadius="base"
-                  bg={isSelected ? 'invokeBlue.500' : 'base.600'}
-                  color={isSelected ? 'base.50' : 'base.100'}
+                  bg={isSelected ? 'invokeBlue.400' : 'base.600'}
+                  color={isSelected ? 'base.800' : 'base.100'}
                   lineHeight="short"
                   fontSize="xs"
                   fontWeight={isSelected ? 'bold' : 'normal'}

invokeai/frontend/web/src/features/metadata/components/MetadataItemView.tsx

@@ -15,7 +15,7 @@ export const MetadataItemView = memo(
     return (
       <Flex gap={2}>
         {onRecall && <RecallButton label={label} onClick={onRecall} isDisabled={isDisabled} />}
-        <Flex direction={direction}>
+        <Flex direction={direction} fontSize="sm">
           <Text fontWeight="semibold" whiteSpace="pre-wrap" pr={2}>
             {label}:
           </Text>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/AdvancedImport.tsx

@@ -1,228 +0,0 @@
-import { Button, Flex, FormControl, FormErrorMessage, FormLabel, Input, Text, Textarea } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
-import BaseModelSelect from 'features/modelManagerV2/subpanels/ModelPanel/Fields/BaseModelSelect';
-import BooleanSelect from 'features/modelManagerV2/subpanels/ModelPanel/Fields/BooleanSelect';
-import ModelFormatSelect from 'features/modelManagerV2/subpanels/ModelPanel/Fields/ModelFormatSelect';
-import ModelTypeSelect from 'features/modelManagerV2/subpanels/ModelPanel/Fields/ModelTypeSelect';
-import ModelVariantSelect from 'features/modelManagerV2/subpanels/ModelPanel/Fields/ModelVariantSelect';
-import PredictionTypeSelect from 'features/modelManagerV2/subpanels/ModelPanel/Fields/PredictionTypeSelect';
-import RepoVariantSelect from 'features/modelManagerV2/subpanels/ModelPanel/Fields/RepoVariantSelect';
-import { addToast } from 'features/system/store/systemSlice';
-import { makeToast } from 'features/system/util/makeToast';
-import { isNil, omitBy } from 'lodash-es';
-import { useCallback, useEffect } from 'react';
-import type { SubmitHandler } from 'react-hook-form';
-import { useForm } from 'react-hook-form';
-import { useTranslation } from 'react-i18next';
-import { useInstallModelMutation } from 'services/api/endpoints/models';
-import type { AnyModelConfig } from 'services/api/types';
-
-export const AdvancedImport = () => {
-  const dispatch = useAppDispatch();
-
-  const [installModel] = useInstallModelMutation();
-
-  const { t } = useTranslation();
-
-  const {
-    register,
-    handleSubmit,
-    control,
-    formState: { errors },
-    setValue,
-    resetField,
-    reset,
-    watch,
-  } = useForm<AnyModelConfig>({
-    defaultValues: {
-      name: '',
-      base: 'sd-1',
-      type: 'main',
-      path: '',
-      description: '',
-      format: 'diffusers',
-      vae: '',
-      variant: 'normal',
-    },
-    mode: 'onChange',
-  });
-
-  const onSubmit = useCallback<SubmitHandler<AnyModelConfig>>(
-    (values) => {
-      installModel({
-        source: values.path,
-        config: omitBy(values, isNil),
-      })
-        .unwrap()
-        .then((_) => {
-          dispatch(
-            addToast(
-              makeToast({
-                title: t('modelManager.modelAdded', {
-                  modelName: values.name,
-                }),
-                status: 'success',
-              })
-            )
-          );
-          reset();
-        })
-        .catch((error) => {
-          if (error) {
-            dispatch(
-              addToast(
-                makeToast({
-                  title: t('toast.modelAddFailed'),
-                  status: 'error',
-                })
-              )
-            );
-          }
-        });
-    },
-    [installModel, dispatch, t, reset]
-  );
-
-  const watchedModelType = watch('type');
-  const watchedModelFormat = watch('format');
-
-  useEffect(() => {
-    if (watchedModelType === 'main') {
-      setValue('format', 'diffusers');
-      setValue('repo_variant', '');
-      setValue('variant', 'normal');
-    }
-    if (watchedModelType === 'lora') {
-      setValue('format', 'lycoris');
-    } else if (watchedModelType === 'embedding') {
-      setValue('format', 'embedding_file');
-    } else if (watchedModelType === 'ip_adapter') {
-      setValue('format', 'invokeai');
-    } else {
-      setValue('format', 'diffusers');
-    }
-    resetField('upcast_attention');
-    resetField('ztsnr_training');
-    resetField('vae');
-    resetField('config');
-    resetField('prediction_type');
-    resetField('image_encoder_model_id');
-  }, [watchedModelType, resetField, setValue]);
-
-  return (
-    <ScrollableContent>
-      <form onSubmit={handleSubmit(onSubmit)}>
-        <Flex flexDirection="column" gap={4} width="100%" pb={10}>
-          <Flex alignItems="flex-end" gap="4">
-            <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-              <FormLabel>{t('modelManager.modelType')}</FormLabel>
-              <ModelTypeSelect<AnyModelConfig> control={control} name="type" />
-            </FormControl>
-            <Text px="2" fontSize="xs" textAlign="center">
-              {t('modelManager.advancedImportInfo')}
-            </Text>
-          </Flex>
-
-          <Flex p={4} borderRadius={4} bg="base.850" height="100%" direction="column" gap="3">
-            <FormControl isInvalid={Boolean(errors.name)}>
-              <Flex direction="column" width="full">
-                <FormLabel>{t('modelManager.name')}</FormLabel>
-                <Input
-                  {...register('name', {
-                    validate: (value) => value.trim().length >= 3 || 'Must be at least 3 characters',
-                  })}
-                />
-                {errors.name?.message && <FormErrorMessage>{errors.name?.message}</FormErrorMessage>}
-              </Flex>
-            </FormControl>
-            <Flex>
-              <FormControl>
-                <Flex direction="column" width="full">
-                  <FormLabel>{t('modelManager.description')}</FormLabel>
-                  <Textarea size="sm" {...register('description')} />
-                  {errors.name?.message && <FormErrorMessage>{errors.name?.message}</FormErrorMessage>}
-                </Flex>
-              </FormControl>
-            </Flex>
-            <Flex gap={4}>
-              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                <FormLabel>{t('modelManager.baseModel')}</FormLabel>
-                <BaseModelSelect control={control} name="base" />
-              </FormControl>
-              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                <FormLabel>{t('common.format')}</FormLabel>
-                <ModelFormatSelect control={control} name="format" />
-              </FormControl>
-            </Flex>
-            <Flex gap={4}>
-              <FormControl flexDir="column" alignItems="flex-start" gap={1} isInvalid={Boolean(errors.path)}>
-                <FormLabel>{t('modelManager.path')}</FormLabel>
-                <Input
-                  {...register('path', {
-                    validate: (value) => value.trim().length > 0 || 'Must provide a path',
-                  })}
-                />
-                {errors.path?.message && <FormErrorMessage>{errors.path?.message}</FormErrorMessage>}
-              </FormControl>
-            </Flex>
-            {watchedModelType === 'main' && (
-              <>
-                <Flex gap={4}>
-                  {watchedModelFormat === 'diffusers' && (
-                    <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                      <FormLabel>{t('modelManager.repoVariant')}</FormLabel>
-                      <RepoVariantSelect<AnyModelConfig> control={control} name="repo_variant" />
-                    </FormControl>
-                  )}
-                  {watchedModelFormat === 'checkpoint' && (
-                    <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                      <FormLabel>{t('modelManager.pathToConfig')}</FormLabel>
-                      <Input {...register('config')} />
-                    </FormControl>
-                  )}
-
-                  <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                    <FormLabel>{t('modelManager.variant')}</FormLabel>
-                    <ModelVariantSelect<AnyModelConfig> control={control} name="variant" />
-                  </FormControl>
-                </Flex>
-                <Flex gap={4}>
-                  <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                    <FormLabel>{t('modelManager.predictionType')}</FormLabel>
-                    <PredictionTypeSelect<AnyModelConfig> control={control} name="prediction_type" />
-                  </FormControl>
-                  <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                    <FormLabel>{t('modelManager.upcastAttention')}</FormLabel>
-                    <BooleanSelect<AnyModelConfig> control={control} name="upcast_attention" />
-                  </FormControl>
-                </Flex>
-                <Flex gap={4}>
-                  <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                    <FormLabel>{t('modelManager.ztsnrTraining')}</FormLabel>
-                    <BooleanSelect<AnyModelConfig> control={control} name="ztsnr_training" />
-                  </FormControl>
-                  <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                    <FormLabel>{t('modelManager.vaeLocation')}</FormLabel>
-                    <Input {...register('vae')} />
-                  </FormControl>
-                </Flex>
-              </>
-            )}
-            {watchedModelType === 'ip_adapter' && (
-              <Flex gap={4}>
-                <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                  <FormLabel>{t('modelManager.imageEncoderModelId')}</FormLabel>
-                  <Input {...register('image_encoder_model_id')} />
-                </FormControl>
-              </Flex>
-            )}
-            <Button mt={2} type="submit">
-              {t('modelManager.addModel')}
-            </Button>
-          </Flex>
-        </Flex>
-      </form>
-    </ScrollableContent>
-  );
-};

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/InstallModelForm.tsx

@@ -12,7 +12,7 @@ type SimpleImportModelConfig = {
   location: string;
 };
 
-export const SimpleImport = () => {
+export const InstallModelForm = () => {
   const dispatch = useAppDispatch();
 
   const [installModel, { isLoading }] = useInstallModelMutation();

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/ModelInstallQueue/ModelInstallQueue.tsx

@@ -5,19 +5,19 @@ import { addToast } from 'features/system/store/systemSlice';
 import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
 import { useCallback, useMemo } from 'react';
-import { useGetModelImportsQuery, usePruneModelImportsMutation } from 'services/api/endpoints/models';
+import { useListModelInstallsQuery, usePruneCompletedModelInstallsMutation } from 'services/api/endpoints/models';
 
-import { ImportQueueItem } from './ImportQueueItem';
+import { ModelInstallQueueItem } from './ModelInstallQueueItem';
 
-export const ImportQueue = () => {
+export const ModelInstallQueue = () => {
   const dispatch = useAppDispatch();
 
-  const { data } = useGetModelImportsQuery();
+  const { data } = useListModelInstallsQuery();
 
-  const [pruneModelImports] = usePruneModelImportsMutation();
+  const [_pruneCompletedModelInstalls] = usePruneCompletedModelInstallsMutation();
 
-  const pruneQueue = useCallback(() => {
-    pruneModelImports()
+  const pruneCompletedModelInstalls = useCallback(() => {
+    _pruneCompletedModelInstalls()
       .unwrap()
       .then((_) => {
         dispatch(
@@ -41,7 +41,7 @@ export const ImportQueue = () => {
           );
         }
       });
-  }, [pruneModelImports, dispatch]);
+  }, [_pruneCompletedModelInstalls, dispatch]);
 
   const pruneAvailable = useMemo(() => {
     return data?.some(
@@ -53,14 +53,19 @@ export const ImportQueue = () => {
     <Flex flexDir="column" p={3} h="full">
       <Flex justifyContent="space-between" alignItems="center">
         <Text>{t('modelManager.importQueue')}</Text>
-        <Button size="sm" isDisabled={!pruneAvailable} onClick={pruneQueue} tooltip={t('modelManager.pruneTooltip')}>
+        <Button
+          size="sm"
+          isDisabled={!pruneAvailable}
+          onClick={pruneCompletedModelInstalls}
+          tooltip={t('modelManager.pruneTooltip')}
+        >
           {t('modelManager.prune')}
         </Button>
       </Flex>
       <Box mt={3} layerStyle="first" p={3} borderRadius="base" w="full" h="full">
         <ScrollableContent>
           <Flex flexDir="column-reverse" gap="2">
-            {data?.map((model) => <ImportQueueItem key={model.id} model={model} />)}
+            {data?.map((model) => <ModelInstallQueueItem key={model.id} installJob={model} />)}
           </Flex>
         </ScrollableContent>
       </Box>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/ModelInstallQueue/ModelInstallQueueBadge.tsx

@@ -6,17 +6,24 @@ import type { ModelInstallStatus } from 'services/api/types';
 const STATUSES = {
   waiting: { colorScheme: 'cyan', translationKey: 'queue.pending' },
   downloading: { colorScheme: 'yellow', translationKey: 'queue.in_progress' },
+  downloads_done: { colorScheme: 'yellow', translationKey: 'queue.in_progress' },
   running: { colorScheme: 'yellow', translationKey: 'queue.in_progress' },
   completed: { colorScheme: 'green', translationKey: 'queue.completed' },
   error: { colorScheme: 'red', translationKey: 'queue.failed' },
   cancelled: { colorScheme: 'orange', translationKey: 'queue.canceled' },
 };
 
-const ImportQueueBadge = ({ status, errorReason }: { status?: ModelInstallStatus; errorReason?: string | null }) => {
+const ModelInstallQueueBadge = ({
+  status,
+  errorReason,
+}: {
+  status?: ModelInstallStatus;
+  errorReason?: string | null;
+}) => {
   const { t } = useTranslation();
 
   if (!status || !Object.keys(STATUSES).includes(status)) {
-    return <></>;
+    return null;
   }
 
   return (
@@ -25,4 +32,4 @@ const ImportQueueBadge = ({ status, errorReason }: { status?: ModelInstallStatus
     </Tooltip>
   );
 };
-export default memo(ImportQueueBadge);
+export default memo(ModelInstallQueueBadge);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/ModelInstallQueue/ModelInstallQueueItem.tsx

@@ -3,15 +3,16 @@ import { useAppDispatch } from 'app/store/storeHooks';
 import { addToast } from 'features/system/store/systemSlice';
 import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
+import { isNil } from 'lodash-es';
 import { useCallback, useMemo } from 'react';
 import { PiXBold } from 'react-icons/pi';
-import { useDeleteModelImportMutation } from 'services/api/endpoints/models';
+import { useCancelModelInstallMutation } from 'services/api/endpoints/models';
 import type { HFModelSource, LocalModelSource, ModelInstallJob, URLModelSource } from 'services/api/types';
 
-import ImportQueueBadge from './ImportQueueBadge';
+import ModelInstallQueueBadge from './ModelInstallQueueBadge';
 
 type ModelListItemProps = {
-  model: ModelInstallJob;
+  installJob: ModelInstallJob;
 };
 
 const formatBytes = (bytes: number) => {
@@ -26,26 +27,26 @@ const formatBytes = (bytes: number) => {
   return `${bytes.toFixed(2)} ${units[i]}`;
 };
 
-export const ImportQueueItem = (props: ModelListItemProps) => {
-  const { model } = props;
+export const ModelInstallQueueItem = (props: ModelListItemProps) => {
+  const { installJob } = props;
   const dispatch = useAppDispatch();
 
-  const [deleteImportModel] = useDeleteModelImportMutation();
+  const [deleteImportModel] = useCancelModelInstallMutation();
 
   const source = useMemo(() => {
-    if (model.source.type === 'hf') {
-      return model.source as HFModelSource;
-    } else if (model.source.type === 'local') {
-      return model.source as LocalModelSource;
-    } else if (model.source.type === 'url') {
-      return model.source as URLModelSource;
+    if (installJob.source.type === 'hf') {
+      return installJob.source as HFModelSource;
+    } else if (installJob.source.type === 'local') {
+      return installJob.source as LocalModelSource;
+    } else if (installJob.source.type === 'url') {
+      return installJob.source as URLModelSource;
     } else {
-      return model.source as LocalModelSource;
+      return installJob.source as LocalModelSource;
     }
-  }, [model.source]);
+  }, [installJob.source]);
 
   const handleDeleteModelImport = useCallback(() => {
-    deleteImportModel(model.id)
+    deleteImportModel(installJob.id)
       .unwrap()
       .then((_) => {
         dispatch(
@@ -69,7 +70,7 @@ export const ImportQueueItem = (props: ModelListItemProps) => {
           );
         }
       });
-  }, [deleteImportModel, model, dispatch]);
+  }, [deleteImportModel, installJob, dispatch]);
 
   const modelName = useMemo(() => {
     switch (source.type) {
@@ -85,19 +86,23 @@ export const ImportQueueItem = (props: ModelListItemProps) => {
   }, [source]);
 
   const progressValue = useMemo(() => {
-    if (model.bytes === undefined || model.total_bytes === undefined) {
+    if (isNil(installJob.bytes) || isNil(installJob.total_bytes)) {
+      return null;
+    }
+
+    if (installJob.total_bytes === 0) {
       return 0;
     }
 
-    return (model.bytes / model.total_bytes) * 100;
-  }, [model.bytes, model.total_bytes]);
+    return (installJob.bytes / installJob.total_bytes) * 100;
+  }, [installJob.bytes, installJob.total_bytes]);
 
   const progressString = useMemo(() => {
-    if (model.status !== 'downloading' || model.bytes === undefined || model.total_bytes === undefined) {
+    if (installJob.status !== 'downloading' || installJob.bytes === undefined || installJob.total_bytes === undefined) {
       return '';
     }
-    return `${formatBytes(model.bytes)} / ${formatBytes(model.total_bytes)}`;
-  }, [model.bytes, model.total_bytes, model.status]);
+    return `${formatBytes(installJob.bytes)} / ${formatBytes(installJob.total_bytes)}`;
+  }, [installJob.bytes, installJob.total_bytes, installJob.status]);
 
   return (
     <Flex gap="2" w="full" alignItems="center">
@@ -109,19 +114,21 @@ export const ImportQueueItem = (props: ModelListItemProps) => {
       <Flex flexDir="column" flex={1}>
         <Tooltip label={progressString}>
           <Progress
-            value={progressValue}
-            isIndeterminate={progressValue === undefined}
+            value={progressValue ?? 0}
+            isIndeterminate={progressValue === null}
             aria-label={t('accessibility.invokeProgressBar')}
             h={2}
           />
         </Tooltip>
       </Flex>
       <Box minW="100px" textAlign="center">
-        <ImportQueueBadge status={model.status} errorReason={model.error_reason} />
+        <ModelInstallQueueBadge status={installJob.status} errorReason={installJob.error_reason} />
       </Box>
 
       <Box minW="20px">
-        {(model.status === 'downloading' || model.status === 'waiting' || model.status === 'running') && (
+        {(installJob.status === 'downloading' ||
+          installJob.status === 'waiting' ||
+          installJob.status === 'running') && (
           <IconButton
             isRound={true}
             size="xs"

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/ScanFolder/ScanFolderForm.tsx

@@ -2,24 +2,24 @@ import { Button, Flex, FormControl, FormErrorMessage, FormLabel, Input } from '@
 import type { ChangeEventHandler } from 'react';
 import { useCallback, useState } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useLazyScanModelsQuery } from 'services/api/endpoints/models';
+import { useLazyScanFolderQuery } from 'services/api/endpoints/models';
 
-import { ScanModelsResults } from './ScanModelsResults';
+import { ScanModelsResults } from './ScanFolderResults';
 
 export const ScanModelsForm = () => {
   const [scanPath, setScanPath] = useState('');
   const [errorMessage, setErrorMessage] = useState('');
   const { t } = useTranslation();
 
-  const [_scanModels, { isLoading, data }] = useLazyScanModelsQuery();
+  const [_scanFolder, { isLoading, data }] = useLazyScanFolderQuery();
 
-  const handleSubmitScan = useCallback(async () => {
-    _scanModels({ scan_path: scanPath }).catch((error) => {
+  const scanFolder = useCallback(async () => {
+    _scanFolder({ scan_path: scanPath }).catch((error) => {
       if (error) {
         setErrorMessage(error.data.detail);
       }
     });
-  }, [_scanModels, scanPath]);
+  }, [_scanFolder, scanPath]);
 
   const handleSetScanPath: ChangeEventHandler<HTMLInputElement> = useCallback((e) => {
     setScanPath(e.target.value);
@@ -36,7 +36,7 @@ export const ScanModelsForm = () => {
               <Input value={scanPath} onChange={handleSetScanPath} />
             </Flex>
 
-            <Button onClick={handleSubmitScan} isLoading={isLoading} isDisabled={scanPath.length === 0}>
+            <Button onClick={scanFolder} isLoading={isLoading} isDisabled={scanPath.length === 0}>
               {t('modelManager.scanFolder')}
             </Button>
           </Flex>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/AddModelPanel/ScanFolder/ScanFolderResults.tsx

@@ -18,7 +18,7 @@ import { useTranslation } from 'react-i18next';
 import { PiXBold } from 'react-icons/pi';
 import { type ScanFolderResponse, useInstallModelMutation } from 'services/api/endpoints/models';
 
-import { ScanModelResultItem } from './ScanModelResultItem';
+import { ScanModelResultItem } from './ScanFolderResultItem';
 
 type ScanModelResultsProps = {
   results: ScanFolderResponse;

invokeai/frontend/web/src/features/modelManagerV2/subpanels/InstallModels.tsx

@@ -1,12 +1,11 @@
 import { Box, Flex, Heading, Tab, TabList, TabPanel, TabPanels, Tabs } from '@invoke-ai/ui-library';
 import { useTranslation } from 'react-i18next';
 
-import { AdvancedImport } from './AddModelPanel/AdvancedImport';
-import { ImportQueue } from './AddModelPanel/ImportQueue/ImportQueue';
-import { ScanModelsForm } from './AddModelPanel/ScanModels/ScanModelsForm';
-import { SimpleImport } from './AddModelPanel/SimpleImport';
+import { InstallModelForm } from './AddModelPanel/InstallModelForm';
+import { ModelInstallQueue } from './AddModelPanel/ModelInstallQueue/ModelInstallQueue';
+import { ScanModelsForm } from './AddModelPanel/ScanFolder/ScanFolderForm';
 
-export const ImportModels = () => {
+export const InstallModels = () => {
   const { t } = useTranslation();
   return (
     <Flex layerStyle="first" p={3} borderRadius="base" w="full" h="full" flexDir="column" gap={2}>
@@ -17,15 +16,11 @@ export const ImportModels = () => {
         <Tabs variant="collapse" height="100%">
           <TabList>
             <Tab>{t('common.simple')}</Tab>
-            <Tab>{t('modelManager.advanced')}</Tab>
             <Tab>{t('modelManager.scan')}</Tab>
           </TabList>
           <TabPanels p={3} height="100%">
             <TabPanel>
-              <SimpleImport />
-            </TabPanel>
-            <TabPanel height="100%">
-              <AdvancedImport />
+              <InstallModelForm />
             </TabPanel>
             <TabPanel height="100%">
               <ScanModelsForm />
@@ -34,7 +29,7 @@ export const ImportModels = () => {
         </Tabs>
       </Box>
       <Box layerStyle="second" borderRadius="base" w="full" h="50%">
-        <ImportQueue />
+        <ModelInstallQueue />
       </Box>
     </Flex>
   );

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelImage.tsx

@@ -0,0 +1,27 @@
+import { Box, Image } from '@invoke-ai/ui-library';
+import { typedMemo } from 'common/util/typedMemo';
+
+type Props = {
+  image_url?: string;
+};
+
+const ModelImage = ({ image_url }: Props) => {
+  if (!image_url) {
+    return <Box height="50px" minWidth="50px" />;
+  }
+
+  return (
+    <Image
+      src={image_url}
+      objectFit="cover"
+      objectPosition="50% 50%"
+      height="50px"
+      width="50px"
+      minHeight="50px"
+      minWidth="50px"
+      borderRadius="base"
+    />
+  );
+};
+
+export default typedMemo(ModelImage);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelListItem.tsx

@@ -22,6 +22,8 @@ import { PiTrashSimpleBold } from 'react-icons/pi';
 import { useDeleteModelsMutation } from 'services/api/endpoints/models';
 import type { AnyModelConfig } from 'services/api/types';
 
+import ModelImage from './ModelImage';
+
 type ModelListItemProps = {
   model: AnyModelConfig;
 };
@@ -73,6 +75,7 @@ const ModelListItem = (props: ModelListItemProps) => {
 
   return (
     <Flex gap={2} alignItems="center" w="full">
+      <ModelImage image_url={model.cover_image || ''} />
       <Flex
         as={Button}
         isChecked={isSelected}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelTypeFilter.tsx

@@ -5,7 +5,7 @@ import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { IoFilter } from 'react-icons/io5';
 
-export const MODEL_TYPE_LABELS: { [key: string]: string } = {
+const MODEL_TYPE_LABELS: { [key: string]: string } = {
   main: 'Main',
   lora: 'LoRA',
   embedding: 'Textual Inversion',