Run ruff format

* move defaultModel logic to modelsLoaded and update to work for key instead of name/base/type string

* lint fix

---------

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>

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

@@ -26,7 +26,6 @@ from ..services.invocation_services import InvocationServices
 from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
 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
@@ -93,10 +92,9 @@ class ApiDependencies:
             ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
         )
         download_queue_service = DownloadQueueService(event_bus=events)
-        model_metadata_service = ModelMetadataStoreSQL(db=db)
         model_manager = ModelManagerService.build_model_manager(
             app_config=configuration,
-            model_record_service=ModelRecordServiceSQL(db=db, metadata_store=model_metadata_service),
+            model_record_service=ModelRecordServiceSQL(db=db),
             download_queue=download_queue_service,
             events=events,
         )

invokeai/app/api/routers/model_manager.py

@@ -3,9 +3,7 @@
 
 import pathlib
 import shutil
-from hashlib import sha1
-from random import randbytes
-from typing import Any, Dict, List, Optional, Set
+from typing import Any, Dict, List, Optional
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
@@ -14,15 +12,11 @@ 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,
@@ -31,9 +25,6 @@ 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
@@ -49,15 +40,6 @@ 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 +50,16 @@ 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 +68,12 @@ 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
 ##############################################################################
@@ -212,89 +153,16 @@ async def get_model_record(
         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 +234,6 @@ 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 +250,13 @@ 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))
@@ -415,14 +268,14 @@ async def update_model_record(
 
 @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 +296,39 @@ async def del_model_record(
         raise HTTPException(status_code=404, detail=str(e))
 
 
-@model_manager_router.post(
-    "/i/",
-    operation_id="add_model_record",
-    responses={
-        201: {
-            "description": "The model added successfully",
-            "content": {"application/json": {"example": example_model_config}},
-        },
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
-        415: {"description": "Unrecognized file/folder format"},
-    },
-    status_code=201,
-)
-async def add_model_record(
-    config: Annotated[
-        AnyModelConfig, Body(description="Model config", discriminator="type", example=example_model_input)
-    ],
-) -> AnyModelConfig:
-    """Add a model using the configuration information appropriate for its type."""
-    logger = ApiDependencies.invoker.services.logger
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    if config.key == "<NOKEY>":
-        config.key = sha1(randbytes(100)).hexdigest()
-        logger.info(f"Created model {config.key} for {config.name}")
-    try:
-        record_store.add_model(config.key, config)
-    except DuplicateModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
+# @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
+#     # now fetch it out
+#     result: AnyModelConfig = record_store.get_model(config.key)
+#     return result
 
 
 @model_manager_router.post(
@@ -553,10 +403,10 @@ 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 +420,8 @@ 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 +430,7 @@ 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 +450,7 @@ 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 +468,8 @@ 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"},
@@ -699,7 +548,8 @@ 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 +558,7 @@ 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 +566,6 @@ 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 +577,66 @@ 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/invocations/compel.py

@@ -5,15 +5,7 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
-from invokeai.app.invocations.fields import (
-    ConditioningField,
-    FieldDescriptions,
-    Input,
-    InputField,
-    MaskField,
-    OutputField,
-    UIComponent,
-)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
@@ -44,7 +36,7 @@ from .model import ClipField
     title="Prompt",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.2.0",
+    version="1.0.1",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -59,10 +51,6 @@ class CompelInvocation(BaseInvocation):
         description=FieldDescriptions.clip,
         input=Input.Connection,
     )
-    mask: Optional[MaskField] = InputField(
-        default=None, description="A mask defining the region that this conditioning prompt applies to."
-    )
-    mask_weight: float = InputField(default=1.0, description="")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -130,13 +118,7 @@ class CompelInvocation(BaseInvocation):
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-                mask=self.mask,
-                mask_weight=self.mask_weight,
-            )
-        )
+        return ConditioningOutput.build(conditioning_name)
 
 
 class SDXLPromptInvocationBase:
@@ -250,7 +232,7 @@ class SDXLPromptInvocationBase:
     title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.2.0",
+    version="1.0.1",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -274,11 +256,6 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
     clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
-    mask: Optional[MaskField] = InputField(
-        default=None, description="A mask defining the region that this conditioning prompt applies to."
-    )
-    mask_weight: float = InputField(default=1.0, description="")
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
         c1, c1_pooled, ec1 = self.run_clip_compel(
@@ -340,13 +317,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-                mask=self.mask,
-                mask_weight=self.mask_weight,
-            )
-        )
+        return ConditioningOutput.build(conditioning_name)
 
 
 @invocation(
@@ -395,7 +366,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 
         conditioning_name = context.conditioning.save(conditioning_data)
 
-        return ConditioningOutput(conditioning=ConditioningField(conditioning_name=conditioning_name, mask_weight=1.0))
+        return ConditioningOutput.build(conditioning_name)
 
 
 @invocation_output("clip_skip_output")

invokeai/app/invocations/conditioning.py

@@ -1,40 +0,0 @@
-import torch
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    InvocationContext,
-    invocation,
-)
-from invokeai.app.invocations.fields import InputField, WithMetadata
-from invokeai.app.invocations.primitives import MaskField, MaskOutput
-
-
-@invocation(
-    "rectangle_mask",
-    title="Create Rectangle Mask",
-    tags=["conditioning"],
-    category="conditioning",
-    version="1.0.0",
-)
-class RectangleMaskInvocation(BaseInvocation, WithMetadata):
-    """Create a rectangular mask."""
-
-    height: int = InputField(description="The height of the entire mask.")
-    width: int = InputField(description="The width of the entire mask.")
-    y_top: int = InputField(description="The top y-coordinate of the rectangular masked region (inclusive).")
-    x_left: int = InputField(description="The left x-coordinate of the rectangular masked region (inclusive).")
-    rectangle_height: int = InputField(description="The height of the rectangular masked region.")
-    rectangle_width: int = InputField(description="The width of the rectangular masked region.")
-
-    def invoke(self, context: InvocationContext) -> MaskOutput:
-        mask = torch.zeros((1, self.height, self.width), dtype=torch.bool)
-        mask[
-            :, self.y_top : self.y_top + self.rectangle_height, self.x_left : self.x_left + self.rectangle_width
-        ] = True
-
-        mask_name = context.tensors.save(mask)
-        return MaskOutput(
-            mask=MaskField(mask_name=mask_name),
-            width=self.width,
-            height=self.height,
-        )

invokeai/app/invocations/fields.py

@@ -194,12 +194,6 @@ class BoardField(BaseModel):
     board_id: str = Field(description="The id of the board")
 
 
-class MaskField(BaseModel):
-    """A mask primitive field."""
-
-    mask_name: str = Field(description="The name of the mask.")
-
-
 class DenoiseMaskField(BaseModel):
     """An inpaint mask field"""
 
@@ -231,12 +225,7 @@ class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
     conditioning_name: str = Field(description="The name of conditioning tensor")
-    mask: Optional[MaskField] = Field(
-        default=None,
-        description="The bool mask associated with this conditioning tensor. Excluded regions should be set to False, "
-        "included regions should be set to True.",
-    )
-    mask_weight: float = Field(description="")
+    # endregion
 
 
 class MetadataField(RootModel):

invokeai/app/invocations/latent.py

@@ -1,5 +1,5 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-import inspect
+
 import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
@@ -9,7 +9,6 @@ import einops
 import numpy as np
 import numpy.typing as npt
 import torch
-import torchvision
 import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.configuration_utils import ConfigMixin
@@ -56,14 +55,7 @@ from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    BasicConditioningInfo,
-    IPAdapterConditioningInfo,
-    Range,
-    SDXLConditioningInfo,
-    TextConditioningData,
-    TextConditioningRegions,
-)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 from ...backend.stable_diffusion.diffusers_pipeline import (
@@ -73,6 +65,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
     T2IAdapterData,
     image_resized_to_grid_as_tensor,
 )
+from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
@@ -291,11 +284,11 @@ def get_scheduler(
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
 
-    positive_conditioning: Union[ConditioningField, list[ConditioningField]] = InputField(
+    positive_conditioning: ConditioningField = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection, ui_order=0
     )
-    negative_conditioning: Union[ConditioningField, list[ConditioningField]] = InputField(
-        description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=0
+    negative_conditioning: ConditioningField = InputField(
+        description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
     )
     noise: Optional[LatentsField] = InputField(
         default=None,
@@ -372,190 +365,39 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 raise ValueError("cfg_scale must be greater than 1")
         return v
 
-    def _get_text_embeddings_and_masks(
-        self,
-        cond_list: list[ConditioningField],
-        context: InvocationContext,
-        device: torch.device,
-        dtype: torch.dtype,
-    ) -> tuple[Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]], list[Optional[torch.Tensor]]]:
-        """Get the text embeddings and masks from the input conditioning fields."""
-        text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]] = []
-        text_embeddings_masks: list[Optional[torch.Tensor]] = []
-        for cond in cond_list:
-            cond_data = context.conditioning.load(cond.conditioning_name)
-            text_embeddings.append(cond_data.conditionings[0].to(device=device, dtype=dtype))
-
-            mask = cond.mask
-            if mask is not None:
-                mask = context.tensors.load(mask.mask_name)
-            text_embeddings_masks.append(mask)
-
-        return text_embeddings, text_embeddings_masks
-
-    def _preprocess_regional_prompt_mask(
-        self, mask: Optional[torch.Tensor], target_height: int, target_width: int
-    ) -> torch.Tensor:
-        """Preprocess a regional prompt mask to match the target height and width.
-
-        If mask is None, returns a mask of all ones with the target height and width.
-        If mask is not None, resizes the mask to the target height and width using nearest neighbor interpolation.
-
-        Returns:
-            torch.Tensor: The processed mask. dtype: torch.bool, shape: (1, 1, target_height, target_width).
-        """
-        if mask is None:
-            return torch.ones((1, 1, target_height, target_width), dtype=torch.bool)
-
-        tf = torchvision.transforms.Resize(
-            (target_height, target_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
-        )
-        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
-        mask = tf(mask)
-
-        return mask
-
-    def concat_regional_text_embeddings(
-        self,
-        text_conditionings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]],
-        masks: Optional[list[Optional[torch.Tensor]]],
-        conditioning_fields: list[ConditioningField],
-        latent_height: int,
-        latent_width: int,
-    ) -> tuple[Union[BasicConditioningInfo, SDXLConditioningInfo], Optional[TextConditioningRegions]]:
-        """Concatenate regional text embeddings into a single embedding and track the region masks accordingly."""
-        if masks is None:
-            masks = [None] * len(text_conditionings)
-        assert len(text_conditionings) == len(masks)
-
-        is_sdxl = type(text_conditionings[0]) is SDXLConditioningInfo
-
-        all_masks_are_none = all(mask is None for mask in masks)
-
-        text_embedding = []
-        pooled_embedding = None
-        add_time_ids = None
-        cur_text_embedding_len = 0
-        processed_masks = []
-        embedding_ranges = []
-        extra_conditioning = None
-
-        for prompt_idx, text_embedding_info in enumerate(text_conditionings):
-            mask = masks[prompt_idx]
-            if (
-                text_embedding_info.extra_conditioning is not None
-                and text_embedding_info.extra_conditioning.wants_cross_attention_control
-            ):
-                extra_conditioning = text_embedding_info.extra_conditioning
-
-            if is_sdxl:
-                # We choose a random SDXLConditioningInfo's pooled_embeds and add_time_ids here, with a preference for
-                # prompts without a mask. We prefer prompts without a mask, because they are more likely to contain
-                # global prompt information.  In an ideal case, there should be exactly one global prompt without a
-                # mask, but we don't enforce this.
-
-                # HACK(ryand): The fact that we have to choose a single pooled_embedding and add_time_ids here is a
-                # fundamental interface issue. The SDXL Compel nodes are not designed to be used in the way that we use
-                # them for regional prompting. Ideally, the DenoiseLatents invocation should accept a single
-                # pooled_embeds tensor and a list of standard text embeds with region masks. This change would be a
-                # pretty major breaking change to a popular node, so for now we use this hack.
-                if pooled_embedding is None or mask is None:
-                    pooled_embedding = text_embedding_info.pooled_embeds
-                if add_time_ids is None or mask is None:
-                    add_time_ids = text_embedding_info.add_time_ids
-
-            text_embedding.append(text_embedding_info.embeds)
-            if not all_masks_are_none:
-                # embedding_ranges.append(
-                #     Range(
-                #         start=cur_text_embedding_len, end=cur_text_embedding_len + text_embedding_info.embeds.shape[1]
-                #     )
-                # )
-                # HACK(ryand): Contrary to its name, tokens_count_including_eos_bos does not seem to include eos and bos
-                # in the count.
-                embedding_ranges.append(
-                    Range(
-                        start=cur_text_embedding_len + 1,
-                        end=cur_text_embedding_len
-                        + text_embedding_info.extra_conditioning.tokens_count_including_eos_bos,
-                    )
-                )
-                processed_masks.append(self._preprocess_regional_prompt_mask(mask, latent_height, latent_width))
-
-            cur_text_embedding_len += text_embedding_info.embeds.shape[1]
-
-        text_embedding = torch.cat(text_embedding, dim=1)
-        assert len(text_embedding.shape) == 3  # batch_size, seq_len, token_len
-
-        regions = None
-        if not all_masks_are_none:
-            regions = TextConditioningRegions(
-                masks=torch.cat(processed_masks, dim=1),
-                ranges=embedding_ranges,
-                mask_weights=[x.mask_weight for x in conditioning_fields],
-            )
-
-        if extra_conditioning is not None and len(text_conditionings) > 1:
-            raise ValueError(
-                "Prompt-to-prompt cross-attention control (a.k.a. `swap()`) is not supported when using multiple "
-                "prompts."
-            )
-
-        if is_sdxl:
-            return SDXLConditioningInfo(
-                embeds=text_embedding,
-                extra_conditioning=extra_conditioning,
-                pooled_embeds=pooled_embedding,
-                add_time_ids=add_time_ids,
-            ), regions
-        return BasicConditioningInfo(
-            embeds=text_embedding,
-            extra_conditioning=extra_conditioning,
-        ), regions
-
     def get_conditioning_data(
         self,
         context: InvocationContext,
+        scheduler: Scheduler,
         unet: UNet2DConditionModel,
-        latent_height: int,
-        latent_width: int,
-    ) -> TextConditioningData:
-        # Normalize self.positive_conditioning and self.negative_conditioning to lists.
-        cond_list = self.positive_conditioning
-        if not isinstance(cond_list, list):
-            cond_list = [cond_list]
-        uncond_list = self.negative_conditioning
-        if not isinstance(uncond_list, list):
-            uncond_list = [uncond_list]
+        seed: int,
+    ) -> ConditioningData:
+        positive_cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name)
+        c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
 
-        cond_text_embeddings, cond_text_embedding_masks = self._get_text_embeddings_and_masks(
-            cond_list, context, unet.device, unet.dtype
-        )
+        negative_cond_data = context.conditioning.load(self.negative_conditioning.conditioning_name)
+        uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
 
-        uncond_text_embeddings, uncond_text_embedding_masks = self._get_text_embeddings_and_masks(
-            uncond_list, context, unet.device, unet.dtype
-        )
-        cond_text_embedding, cond_regions = self.concat_regional_text_embeddings(
-            text_conditionings=cond_text_embeddings,
-            masks=cond_text_embedding_masks,
-            conditioning_fields=cond_list,
-            latent_height=latent_height,
-            latent_width=latent_width,
-        )
-        uncond_text_embedding, uncond_regions = self.concat_regional_text_embeddings(
-            text_conditionings=uncond_text_embeddings,
-            masks=uncond_text_embedding_masks,
-            conditioning_fields=uncond_list,
-            latent_height=latent_height,
-            latent_width=latent_width,
-        )
-        conditioning_data = TextConditioningData(
-            uncond_text=uncond_text_embedding,
-            cond_text=cond_text_embedding,
-            uncond_regions=uncond_regions,
-            cond_regions=cond_regions,
+        conditioning_data = ConditioningData(
+            unconditioned_embeddings=uc,
+            text_embeddings=c,
             guidance_scale=self.cfg_scale,
             guidance_rescale_multiplier=self.cfg_rescale_multiplier,
+            postprocessing_settings=PostprocessingSettings(
+                threshold=0.0,  # threshold,
+                warmup=0.2,  # warmup,
+                h_symmetry_time_pct=None,  # h_symmetry_time_pct,
+                v_symmetry_time_pct=None,  # v_symmetry_time_pct,
+            ),
+        )
+
+        conditioning_data = conditioning_data.add_scheduler_args_if_applicable(  # FIXME
+            scheduler,
+            # for ddim scheduler
+            eta=0.0,  # ddim_eta
+            # for ancestral and sde schedulers
+            # flip all bits to have noise different from initial
+            generator=torch.Generator(device=unet.device).manual_seed(seed ^ 0xFFFFFFFF),
         )
         return conditioning_data
 
@@ -661,6 +503,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         self,
         context: InvocationContext,
         ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
+        conditioning_data: ConditioningData,
         exit_stack: ExitStack,
     ) -> Optional[list[IPAdapterData]]:
         """If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
@@ -677,6 +520,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             return None
 
         ip_adapter_data_list = []
+        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)
@@ -699,13 +543,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     single_ipa_images, image_encoder_model
                 )
 
+                conditioning_data.ip_adapter_conditioning.append(
+                    IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
+                )
+
             ip_adapter_data_list.append(
                 IPAdapterData(
                     ip_adapter_model=ip_adapter_model,
                     weight=single_ip_adapter.weight,
                     begin_step_percent=single_ip_adapter.begin_step_percent,
                     end_step_percent=single_ip_adapter.end_step_percent,
-                    ip_adapter_conditioning=IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds),
                 )
             )
 
@@ -795,7 +642,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         steps: int,
         denoising_start: float,
         denoising_end: float,
-        seed: int,
     ) -> Tuple[int, List[int], int]:
         assert isinstance(scheduler, ConfigMixin)
         if scheduler.config.get("cpu_only", False):
@@ -824,15 +670,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
         num_inference_steps = len(timesteps) // scheduler.order
 
-        scheduler_step_kwargs = {}
-        scheduler_step_signature = inspect.signature(scheduler.step)
-        if "generator" in scheduler_step_signature.parameters:
-            # At some point, someone decided that schedulers that accept a generator should use the original seed with
-            # all bits flipped. I don't know the original rationale for this, but now we must keep it like this for
-            # reproducibility.
-            scheduler_step_kwargs = {"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)}
-
-        return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
+        return num_inference_steps, timesteps, init_timestep
 
     def prep_inpaint_mask(
         self, context: InvocationContext, latents: torch.Tensor
@@ -925,10 +763,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 )
 
                 pipeline = self.create_pipeline(unet, scheduler)
-                _, _, latent_height, latent_width = latents.shape
-                conditioning_data = self.get_conditioning_data(
-                    context=context, unet=unet, latent_height=latent_height, latent_width=latent_width
-                )
+                conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
 
                 controlnet_data = self.prep_control_data(
                     context=context,
@@ -942,16 +777,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 ip_adapter_data = self.prep_ip_adapter_data(
                     context=context,
                     ip_adapter=self.ip_adapter,
+                    conditioning_data=conditioning_data,
                     exit_stack=exit_stack,
                 )
 
-                num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
+                num_inference_steps, timesteps, init_timestep = self.init_scheduler(
                     scheduler,
                     device=unet.device,
                     steps=self.steps,
                     denoising_start=self.denoising_start,
                     denoising_end=self.denoising_end,
-                    seed=seed,
                 )
 
                 result_latents = pipeline.latents_from_embeddings(
@@ -964,7 +799,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     masked_latents=masked_latents,
                     gradient_mask=gradient_mask,
                     num_inference_steps=num_inference_steps,
-                    scheduler_step_kwargs=scheduler_step_kwargs,
                     conditioning_data=conditioning_data,
                     control_data=controlnet_data,
                     ip_adapter_data=ip_adapter_data,

invokeai/app/invocations/model.py

@@ -133,7 +133,7 @@ class MainModelLoaderInvocation(BaseInvocation):
             vae=VaeField(
                 vae=ModelInfo(
                     key=key,
-                    submodel_type=SubModelType.Vae,
+                    submodel_type=SubModelType.VAE,
                 ),
             ),
         )

invokeai/app/invocations/primitives.py

@@ -14,7 +14,6 @@ from invokeai.app.invocations.fields import (
     Input,
     InputField,
     LatentsField,
-    MaskField,
     OutputField,
     UIComponent,
 )
@@ -230,18 +229,6 @@ class StringCollectionInvocation(BaseInvocation):
 # region Image
 
 
-@invocation_output("mask_output")
-class MaskOutput(BaseInvocationOutput):
-    """A torch mask tensor.
-    dtype: torch.bool
-    shape: (1, height, width).
-    """
-
-    mask: MaskField = OutputField(description="The mask.")
-    width: int = OutputField(description="The width of the mask in pixels.")
-    height: int = OutputField(description="The height of the mask in pixels.")
-
-
 @invocation_output("image_output")
 class ImageOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
@@ -427,6 +414,10 @@ class ConditioningOutput(BaseInvocationOutput):
 
     conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
 
+    @classmethod
+    def build(cls, conditioning_name: str) -> "ConditioningOutput":
+        return cls(conditioning=ConditioningField(conditioning_name=conditioning_name))
+
 
 @invocation_output("conditioning_collection_output")
 class ConditioningCollectionOutput(BaseInvocationOutput):

invokeai/app/invocations/sdxl.py

@@ -85,7 +85,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
             vae=VaeField(
                 vae=ModelInfo(
                     key=model_key,
-                    submodel_type=SubModelType.Vae,
+                    submodel_type=SubModelType.VAE,
                 ),
             ),
         )
@@ -142,7 +142,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
             vae=VaeField(
                 vae=ModelInfo(
                     key=model_key,
-                    submodel_type=SubModelType.Vae,
+                    submodel_type=SubModelType.VAE,
                 ),
             ),
         )

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/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

@@ -20,12 +20,15 @@ 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 +38,14 @@ 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 +95,6 @@ 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 +143,7 @@ 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 +153,11 @@ 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 +183,7 @@ 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})?(?::/?([^:]+))?)?$"
@@ -374,15 +379,18 @@ 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:
@@ -468,7 +476,7 @@ 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 +528,15 @@ 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 +545,11 @@ 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 +570,15 @@ 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 +606,17 @@ 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 +631,7 @@ 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 +659,7 @@ 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 +846,11 @@ 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_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 +59,33 @@ 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)
+
+
 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 +109,12 @@ 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 +129,6 @@ 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]:
-        """
-        Retrieve metadata (if any) from when model was downloaded from a repo.
-
-        :param key: Model key
-        """
-        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 +203,3 @@ 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,),
@@ -220,7 +213,7 @@ 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 +239,8 @@ 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 +257,13 @@ 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 +272,7 @@ 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 +283,13 @@ 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 +298,35 @@ 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 +337,7 @@ 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/session_processor/session_processor_base.py

@@ -1,6 +1,35 @@
 from abc import ABC, abstractmethod
+from threading import Event
 
+from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
+from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
+
+
+class SessionRunnerBase(ABC):
+    """
+    Base class for session runner.
+    """
+
+    @abstractmethod
+    def start(self, services: InvocationServices, cancel_event: Event) -> None:
+        """Starts the session runner"""
+        pass
+
+    @abstractmethod
+    def run(self, queue_item: SessionQueueItem) -> None:
+        """Runs the session"""
+        pass
+
+    @abstractmethod
+    def complete(self, queue_item: SessionQueueItem) -> None:
+        """Completes the session"""
+        pass
+
+    @abstractmethod
+    def run_node(self, node_id: str, queue_item: SessionQueueItem) -> None:
+        """Runs an already prepared node on the session"""
+        pass
 
 
 class SessionProcessorBase(ABC):

invokeai/app/services/session_processor/session_processor_default.py

@@ -2,13 +2,14 @@ import traceback
 from contextlib import suppress
 from threading import BoundedSemaphore, Thread
 from threading import Event as ThreadEvent
-from typing import Optional
+from typing import Callable, Optional, Union
 
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event as FastAPIEvent
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.events.events_base import EventServiceBase
+from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.invocation_stats.invocation_stats_common import GESStatsNotFoundError
 from invokeai.app.services.session_processor.session_processor_common import CanceledException
 from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
@@ -16,15 +17,164 @@ from invokeai.app.services.shared.invocation_context import InvocationContextDat
 from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
-from .session_processor_base import SessionProcessorBase
+from .session_processor_base import SessionProcessorBase, SessionRunnerBase
 from .session_processor_common import SessionProcessorStatus
 
 
+class DefaultSessionRunner(SessionRunnerBase):
+    """Processes a single session's invocations"""
+
+    def __init__(
+        self,
+        on_before_run_node: Union[Callable[[BaseInvocation, SessionQueueItem], bool], None] = None,
+        on_after_run_node: Union[Callable[[BaseInvocation, SessionQueueItem], bool], None] = None,
+    ):
+        self.on_before_run_node = on_before_run_node
+        self.on_after_run_node = on_after_run_node
+
+    def start(self, services: InvocationServices, cancel_event: ThreadEvent):
+        """Start the session runner"""
+        self.services = services
+        self.cancel_event = cancel_event
+
+    def run(self, queue_item: SessionQueueItem):
+        """Run the graph"""
+        if not queue_item.session:
+            raise ValueError("Queue item has no session")
+        # Loop over invocations until the session is complete or canceled
+        while not (queue_item.session.is_complete() or self.cancel_event.is_set()):
+            # Prepare the next node
+            invocation = queue_item.session.next()
+            if invocation is None:
+                # If there are no more invocations, complete the graph
+                break
+            # Build invocation context (the node-facing API
+            self.run_node(invocation.id, queue_item)
+        self.complete(queue_item)
+
+    def complete(self, queue_item: SessionQueueItem):
+        """Complete the graph"""
+        self.services.events.emit_graph_execution_complete(
+            queue_batch_id=queue_item.batch_id,
+            queue_item_id=queue_item.item_id,
+            queue_id=queue_item.queue_id,
+            graph_execution_state_id=queue_item.session.id,
+        )
+
+    def _on_before_run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
+        """Run before a node is executed"""
+        # Send starting event
+        self.services.events.emit_invocation_started(
+            queue_batch_id=queue_item.batch_id,
+            queue_item_id=queue_item.item_id,
+            queue_id=queue_item.queue_id,
+            graph_execution_state_id=queue_item.session_id,
+            node=invocation.model_dump(),
+            source_node_id=queue_item.session.prepared_source_mapping[invocation.id],
+        )
+        if self.on_before_run_node is not None:
+            self.on_before_run_node(invocation, queue_item)
+
+    def _on_after_run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
+        """Run after a node is executed"""
+        if self.on_after_run_node is not None:
+            self.on_after_run_node(invocation, queue_item)
+
+    def run_node(self, node_id: str, queue_item: SessionQueueItem):
+        """Run a single node in the graph"""
+        # If this error raises a NodeNotFoundError that's handled by the processor
+        invocation = queue_item.session.execution_graph.get_node(node_id)
+        try:
+            self._on_before_run_node(invocation, queue_item)
+            data = InvocationContextData(
+                invocation=invocation,
+                source_invocation_id=queue_item.session.prepared_source_mapping[invocation.id],
+                queue_item=queue_item,
+            )
+
+            # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
+            with self.services.performance_statistics.collect_stats(invocation, queue_item.session_id):
+                context = build_invocation_context(
+                    data=data,
+                    services=self.services,
+                    cancel_event=self.cancel_event,
+                )
+
+                # Invoke the node
+                outputs = invocation.invoke_internal(context=context, services=self.services)
+
+                # Save outputs and history
+                queue_item.session.complete(invocation.id, outputs)
+
+            self._on_after_run_node(invocation, queue_item)
+            # Send complete event on successful runs
+            self.services.events.emit_invocation_complete(
+                queue_batch_id=queue_item.batch_id,
+                queue_item_id=queue_item.item_id,
+                queue_id=queue_item.queue_id,
+                graph_execution_state_id=queue_item.session.id,
+                node=invocation.model_dump(),
+                source_node_id=data.source_invocation_id,
+                result=outputs.model_dump(),
+            )
+        except KeyboardInterrupt:
+            # TODO(MM2): Create an event for this
+            pass
+        except CanceledException:
+            # When the user cancels the graph, we first set the cancel event. The event is checked
+            # between invocations, in this loop. Some invocations are long-running, and we need to
+            # be able to cancel them mid-execution.
+            #
+            # For example, denoising is a long-running invocation with many steps. A step callback
+            # is executed after each step. This step callback checks if the canceled event is set,
+            # then raises a CanceledException to stop execution immediately.
+            #
+            # When we get a CanceledException, we don't need to do anything - just pass and let the
+            # loop go to its next iteration, and the cancel event will be handled correctly.
+            pass
+        except Exception as e:
+            error = traceback.format_exc()
+
+            # Save error
+            queue_item.session.set_node_error(invocation.id, error)
+            self.services.logger.error(
+                f"Error while invoking session {queue_item.session_id}, invocation {invocation.id} ({invocation.get_type()}):\n{e}"
+            )
+            self.services.logger.error(error)
+
+            # Send error event
+            self.services.events.emit_invocation_error(
+                queue_batch_id=queue_item.session_id,
+                queue_item_id=queue_item.item_id,
+                queue_id=queue_item.queue_id,
+                graph_execution_state_id=queue_item.session.id,
+                node=invocation.model_dump(),
+                source_node_id=queue_item.session.prepared_source_mapping[invocation.id],
+                error_type=e.__class__.__name__,
+                error=error,
+            )
+
+
 class DefaultSessionProcessor(SessionProcessorBase):
-    def start(self, invoker: Invoker, thread_limit: int = 1, polling_interval: int = 1) -> None:
+    """Processes sessions from the session queue"""
+
+    def __init__(self, session_runner: Union[SessionRunnerBase, None] = None) -> None:
+        super().__init__()
+        self.session_runner = session_runner if session_runner else DefaultSessionRunner()
+
+    def start(
+        self,
+        invoker: Invoker,
+        thread_limit: int = 1,
+        polling_interval: int = 1,
+        on_before_run_session: Union[Callable[[SessionQueueItem], bool], None] = None,
+        on_after_run_session: Union[Callable[[SessionQueueItem], bool], None] = None,
+    ) -> None:
         self._invoker: Invoker = invoker
         self._queue_item: Optional[SessionQueueItem] = None
         self._invocation: Optional[BaseInvocation] = None
+        self.on_before_run_session = on_before_run_session
+        self.on_after_run_session = on_after_run_session
 
         self._resume_event = ThreadEvent()
         self._stop_event = ThreadEvent()
@@ -59,6 +209,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
                 "cancel_event": self._cancel_event,
             },
         )
+        self.session_runner.start(services=invoker.services, cancel_event=self._cancel_event)
         self._thread.start()
 
     def stop(self, *args, **kwargs) -> None:
@@ -117,131 +268,34 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
                         cancel_event.clear()
 
+                        # If we have a on_before_run_session callback, call it
+                        if self.on_before_run_session is not None:
+                            self.on_before_run_session(self._queue_item)
+
                         # If profiling is enabled, start the profiler
                         if self._profiler is not None:
                             self._profiler.start(profile_id=self._queue_item.session_id)
 
-                        # Prepare invocations and take the first
-                        self._invocation = self._queue_item.session.next()
+                        # Run the graph
+                        self.session_runner.run(queue_item=self._queue_item)
 
-                        # Loop over invocations until the session is complete or canceled
-                        while self._invocation is not None and not cancel_event.is_set():
-                            # get the source node id to provide to clients (the prepared node id is not as useful)
-                            source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
-
-                            # Send starting event
-                            self._invoker.services.events.emit_invocation_started(
-                                queue_batch_id=self._queue_item.batch_id,
-                                queue_item_id=self._queue_item.item_id,
-                                queue_id=self._queue_item.queue_id,
-                                graph_execution_state_id=self._queue_item.session_id,
-                                node=self._invocation.model_dump(),
-                                source_node_id=source_invocation_id,
+                        # If we are profiling, stop the profiler and dump the profile & stats
+                        if self._profiler:
+                            profile_path = self._profiler.stop()
+                            stats_path = profile_path.with_suffix(".json")
+                            self._invoker.services.performance_statistics.dump_stats(
+                                graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
                             )
 
-                            # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
-                            try:
-                                with self._invoker.services.performance_statistics.collect_stats(
-                                    self._invocation, self._queue_item.session.id
-                                ):
-                                    # Build invocation context (the node-facing API)
-                                    data = InvocationContextData(
-                                        invocation=self._invocation,
-                                        source_invocation_id=source_invocation_id,
-                                        queue_item=self._queue_item,
-                                    )
-                                    context = build_invocation_context(
-                                        data=data,
-                                        services=self._invoker.services,
-                                        cancel_event=self._cancel_event,
-                                    )
+                        # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
+                        # we don't care about that - suppress the error.
+                        with suppress(GESStatsNotFoundError):
+                            self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
+                            self._invoker.services.performance_statistics.reset_stats()
 
-                                    # Invoke the node
-                                    outputs = self._invocation.invoke_internal(
-                                        context=context, services=self._invoker.services
-                                    )
-
-                                    # Save outputs and history
-                                    self._queue_item.session.complete(self._invocation.id, outputs)
-
-                                    # Send complete event
-                                    self._invoker.services.events.emit_invocation_complete(
-                                        queue_batch_id=self._queue_item.batch_id,
-                                        queue_item_id=self._queue_item.item_id,
-                                        queue_id=self._queue_item.queue_id,
-                                        graph_execution_state_id=self._queue_item.session.id,
-                                        node=self._invocation.model_dump(),
-                                        source_node_id=source_invocation_id,
-                                        result=outputs.model_dump(),
-                                    )
-
-                            except KeyboardInterrupt:
-                                # TODO(MM2): Create an event for this
-                                pass
-
-                            except CanceledException:
-                                # When the user cancels the graph, we first set the cancel event. The event is checked
-                                # between invocations, in this loop. Some invocations are long-running, and we need to
-                                # be able to cancel them mid-execution.
-                                #
-                                # For example, denoising is a long-running invocation with many steps. A step callback
-                                # is executed after each step. This step callback checks if the canceled event is set,
-                                # then raises a CanceledException to stop execution immediately.
-                                #
-                                # When we get a CanceledException, we don't need to do anything - just pass and let the
-                                # loop go to its next iteration, and the cancel event will be handled correctly.
-                                pass
-
-                            except Exception as e:
-                                error = traceback.format_exc()
-
-                                # Save error
-                                self._queue_item.session.set_node_error(self._invocation.id, error)
-                                self._invoker.services.logger.error(
-                                    f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
-                                )
-                                self._invoker.services.logger.error(error)
-
-                                # Send error event
-                                self._invoker.services.events.emit_invocation_error(
-                                    queue_batch_id=self._queue_item.session_id,
-                                    queue_item_id=self._queue_item.item_id,
-                                    queue_id=self._queue_item.queue_id,
-                                    graph_execution_state_id=self._queue_item.session.id,
-                                    node=self._invocation.model_dump(),
-                                    source_node_id=source_invocation_id,
-                                    error_type=e.__class__.__name__,
-                                    error=error,
-                                )
-                                pass
-
-                            # The session is complete if the all invocations are complete or there was an error
-                            if self._queue_item.session.is_complete() or cancel_event.is_set():
-                                # Send complete event
-                                self._invoker.services.events.emit_graph_execution_complete(
-                                    queue_batch_id=self._queue_item.batch_id,
-                                    queue_item_id=self._queue_item.item_id,
-                                    queue_id=self._queue_item.queue_id,
-                                    graph_execution_state_id=self._queue_item.session.id,
-                                )
-                                # If we are profiling, stop the profiler and dump the profile & stats
-                                if self._profiler:
-                                    profile_path = self._profiler.stop()
-                                    stats_path = profile_path.with_suffix(".json")
-                                    self._invoker.services.performance_statistics.dump_stats(
-                                        graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
-                                    )
-                                # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
-                                # we don't care about that - suppress the error.
-                                with suppress(GESStatsNotFoundError):
-                                    self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
-                                    self._invoker.services.performance_statistics.reset_stats()
-
-                                # Set the invocation to None to prepare for the next session
-                                self._invocation = None
-                            else:
-                                # Prepare the next invocation
-                                self._invocation = self._queue_item.session.next()
+                        # If we have a on_after_run_session callback, call it
+                        if self.on_after_run_session is not None:
+                            self.on_after_run_session(self._queue_item)
 
                         # The session is complete, immediately poll for next session
                         self._queue_item = None
@@ -275,3 +329,4 @@ class DefaultSessionProcessor(SessionProcessorBase):
             poll_now_event.clear()
             self._queue_item = None
             self._thread_semaphore.release()
+            self._invoker.services.logger.debug("Session processor stopped")

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/shared/sqlite_migrator/sqlite_migrator_common.py

@@ -17,7 +17,8 @@ class MigrateCallback(Protocol):
     See :class:`Migration` for an example.
     """
 
-    def __call__(self, cursor: sqlite3.Cursor) -> None: ...
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        ...
 
 
 class MigrationError(RuntimeError):

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/backend/ip_adapter/attention_processor.py

@@ -0,0 +1,182 @@
+# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
+#   and modified as needed
+
+# tencent-ailab comment:
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.models.attention_processor import AttnProcessor2_0 as DiffusersAttnProcessor2_0
+
+from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionProcessorWeights
+
+
+# Create a version of AttnProcessor2_0 that is a sub-class of nn.Module. This is required for IP-Adapter state_dict
+# loading.
+class AttnProcessor2_0(DiffusersAttnProcessor2_0, nn.Module):
+    def __init__(self):
+        DiffusersAttnProcessor2_0.__init__(self)
+        nn.Module.__init__(self)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        ip_adapter_image_prompt_embeds=None,
+    ):
+        """Re-definition of DiffusersAttnProcessor2_0.__call__(...) that accepts and ignores the
+        ip_adapter_image_prompt_embeds parameter.
+        """
+        return DiffusersAttnProcessor2_0.__call__(
+            self, attn, hidden_states, encoder_hidden_states, attention_mask, temb
+        )
+
+
+class IPAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+    """
+
+    def __init__(self, weights: list[IPAttentionProcessorWeights], scales: list[float]):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        assert len(weights) == len(scales)
+
+        self._weights = weights
+        self._scales = scales
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+        ip_adapter_image_prompt_embeds=None,
+    ):
+        """Apply IP-Adapter attention.
+
+        Args:
+            ip_adapter_image_prompt_embeds (torch.Tensor): The image prompt embeddings.
+                Shape: (batch_size, num_ip_images, seq_len, ip_embedding_len).
+        """
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        if encoder_hidden_states is not None:
+            # If encoder_hidden_states is not None, then we are doing cross-attention, not self-attention. In this case,
+            # we will apply IP-Adapter conditioning. We validate the inputs for IP-Adapter conditioning here.
+            assert ip_adapter_image_prompt_embeds is not None
+            assert len(ip_adapter_image_prompt_embeds) == len(self._weights)
+
+            for ipa_embed, ipa_weights, scale in zip(
+                ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True
+            ):
+                # The batch dimensions should match.
+                assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
+                # The token_len dimensions should match.
+                assert ipa_embed.shape[-1] == encoder_hidden_states.shape[-1]
+
+                ip_hidden_states = ipa_embed
+
+                # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
+
+                ip_key = ipa_weights.to_k_ip(ip_hidden_states)
+                ip_value = ipa_weights.to_v_ip(ip_hidden_states)
+
+                # Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
+
+                ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+                ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+                # Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
+
+                # TODO: add support for attn.scale when we move to Torch 2.1
+                ip_hidden_states = F.scaled_dot_product_attention(
+                    query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+                )
+
+                # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
+
+                ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+                ip_hidden_states = ip_hidden_states.to(query.dtype)
+
+                # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
+
+                hidden_states = hidden_states + scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states

invokeai/backend/ip_adapter/unet_patcher.py

@@ -1,55 +1,52 @@
 from contextlib import contextmanager
-from typing import Optional
 
 from diffusers.models import UNet2DConditionModel
 
+from invokeai.backend.ip_adapter.attention_processor import AttnProcessor2_0, IPAttnProcessor2_0
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.stable_diffusion.diffusion.custom_attention import CustomAttnProcessor2_0
 
 
-class UNetAttentionPatcher:
-    """A class for patching a UNet with CustomAttnProcessor2_0 attention layers."""
+class UNetPatcher:
+    """A class that contains multiple IP-Adapters and can apply them to a UNet."""
 
-    def __init__(self, ip_adapters: Optional[list[IPAdapter]]):
+    def __init__(self, ip_adapters: list[IPAdapter]):
         self._ip_adapters = ip_adapters
-        self._ip_adapter_scales = None
-
-        if self._ip_adapters is not None:
-            self._ip_adapter_scales = [1.0] * len(self._ip_adapters)
+        self._scales = [1.0] * len(self._ip_adapters)
 
     def set_scale(self, idx: int, value: float):
-        self._ip_adapter_scales[idx] = value
+        self._scales[idx] = value
 
     def _prepare_attention_processors(self, unet: UNet2DConditionModel):
         """Prepare a dict of attention processors that can be injected into a unet, and load the IP-Adapter attention
-        weights into them (if IP-Adapters are being applied).
+        weights into them.
 
         Note that the `unet` param is only used to determine attention block dimensions and naming.
         """
         # Construct a dict of attention processors based on the UNet's architecture.
         attn_procs = {}
         for idx, name in enumerate(unet.attn_processors.keys()):
-            if name.endswith("attn1.processor") or self._ip_adapters is None:
-                # "attn1" processors do not use IP-Adapters.
-                attn_procs[name] = CustomAttnProcessor2_0()
+            if name.endswith("attn1.processor"):
+                attn_procs[name] = AttnProcessor2_0()
             else:
                 # Collect the weights from each IP Adapter for the idx'th attention processor.
-                attn_procs[name] = CustomAttnProcessor2_0(
+                attn_procs[name] = IPAttnProcessor2_0(
                     [ip_adapter.attn_weights.get_attention_processor_weights(idx) for ip_adapter in self._ip_adapters],
-                    self._ip_adapter_scales,
+                    self._scales,
                 )
         return attn_procs
 
     @contextmanager
     def apply_ip_adapter_attention(self, unet: UNet2DConditionModel):
-        """A context manager that patches `unet` with CustomAttnProcessor2_0 attention layers."""
+        """A context manager that patches `unet` with IP-Adapter attention processors."""
+
         attn_procs = self._prepare_attention_processors(unet)
+
         orig_attn_processors = unet.attn_processors
 
         try:
-            # Note to future devs: set_attn_processor(...) does something slightly unexpected - it pops elements from
-            # the passed dict. So, if you wanted to keep the dict for future use, you'd have to make a
-            # moderately-shallow copy of it. E.g. `attn_procs_copy = {k: v for k, v in attn_procs.items()}`.
+            # Note to future devs: set_attn_processor(...) does something slightly unexpected - it pops elements from the
+            # passed dict. So, if you wanted to keep the dict for future use, you'd have to make a moderately-shallow copy
+            # of it. E.g. `attn_procs_copy = {k: v for k, v in attn_procs.items()}`.
             unet.set_attn_processor(attn_procs)
             yield None
         finally:

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 +115,187 @@ 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
+    )
 
     @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."""
@@ -242,6 +304,10 @@ 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."""
@@ -249,58 +315,65 @@ class CLIPVisionDiffusersConfig(ModelConfigBase):
     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 +405,6 @@ 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/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

@@ -22,8 +22,8 @@ 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)
+@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."""
 

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)
+@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,
@@ -95,8 +97,8 @@ 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 +110,6 @@ 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 +131,21 @@ 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 +157,17 @@ 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 ModelHash().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 +177,7 @@ 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 +211,11 @@ 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 +237,7 @@ 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 +283,21 @@ 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 +503,12 @@ 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 +714,8 @@ 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 +723,8 @@ 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/stable_diffusion/diffusers_pipeline.py

@@ -23,12 +23,9 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    IPAdapterConditioningInfo,
-    TextConditioningData,
-)
+from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
 
 from ..util import auto_detect_slice_size, normalize_device
 
@@ -173,11 +170,10 @@ class ControlNetData:
 
 @dataclass
 class IPAdapterData:
-    ip_adapter_model: IPAdapter
-    ip_adapter_conditioning: IPAdapterConditioningInfo
-
-    # Either a single weight applied to all steps, or a list of weights for each step.
+    ip_adapter_model: IPAdapter = Field(default=None)
+    # TODO: change to polymorphic so can do different weights per step (once implemented...)
     weight: Union[float, List[float]] = Field(default=1.0)
+    # weight: float = Field(default=1.0)
     begin_step_percent: float = Field(default=0.0)
     end_step_percent: float = Field(default=1.0)
 
@@ -318,8 +314,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         self,
         latents: torch.Tensor,
         num_inference_steps: int,
-        scheduler_step_kwargs: dict[str, Any],
-        conditioning_data: TextConditioningData,
+        conditioning_data: ConditioningData,
         *,
         noise: Optional[torch.Tensor],
         timesteps: torch.Tensor,
@@ -379,7 +374,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 latents,
                 timesteps,
                 conditioning_data,
-                scheduler_step_kwargs=scheduler_step_kwargs,
                 additional_guidance=additional_guidance,
                 control_data=control_data,
                 ip_adapter_data=ip_adapter_data,
@@ -399,8 +393,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         self,
         latents: torch.Tensor,
         timesteps,
-        conditioning_data: TextConditioningData,
-        scheduler_step_kwargs: dict[str, Any],
+        conditioning_data: ConditioningData,
         *,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
@@ -417,35 +410,22 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if timesteps.shape[0] == 0:
             return latents
 
-        extra_conditioning_info = conditioning_data.cond_text.extra_conditioning
-        use_cross_attention_control = (
-            extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control
-        )
-        use_ip_adapter = ip_adapter_data is not None
-        use_regional_prompting = (
-            conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None
-        )
-        if use_cross_attention_control and use_ip_adapter:
-            raise ValueError(
-                "Prompt-to-prompt cross-attention control (`.swap()`) and IP-Adapter cannot be used simultaneously."
-            )
-        if use_cross_attention_control and use_regional_prompting:
-            raise ValueError(
-                "Prompt-to-prompt cross-attention control (`.swap()`) and regional prompting cannot be used simultaneously."
-            )
-
-        unet_attention_patcher = None
-        self.use_ip_adapter = use_ip_adapter
-        attn_ctx = nullcontext()
-        if use_cross_attention_control:
+        ip_adapter_unet_patcher = None
+        extra_conditioning_info = conditioning_data.text_embeddings.extra_conditioning
+        if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
             attn_ctx = self.invokeai_diffuser.custom_attention_context(
                 self.invokeai_diffuser.model,
                 extra_conditioning_info=extra_conditioning_info,
             )
-        if use_ip_adapter or use_regional_prompting:
-            ip_adapters = [ipa.ip_adapter_model for ipa in ip_adapter_data] if use_ip_adapter else None
-            unet_attention_patcher = UNetAttentionPatcher(ip_adapters)
-            attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
+            self.use_ip_adapter = False
+        elif ip_adapter_data is not None:
+            # TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active?
+            # As it is now, the IP-Adapter will silently be skipped.
+            ip_adapter_unet_patcher = UNetPatcher([ipa.ip_adapter_model for ipa in ip_adapter_data])
+            attn_ctx = ip_adapter_unet_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
+            self.use_ip_adapter = True
+        else:
+            attn_ctx = nullcontext()
 
         with attn_ctx:
             if callback is not None:
@@ -468,14 +448,22 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     conditioning_data,
                     step_index=i,
                     total_step_count=len(timesteps),
-                    scheduler_step_kwargs=scheduler_step_kwargs,
                     additional_guidance=additional_guidance,
                     control_data=control_data,
                     ip_adapter_data=ip_adapter_data,
                     t2i_adapter_data=t2i_adapter_data,
-                    unet_attention_patcher=unet_attention_patcher,
+                    ip_adapter_unet_patcher=ip_adapter_unet_patcher,
                 )
                 latents = step_output.prev_sample
+
+                latents = self.invokeai_diffuser.do_latent_postprocessing(
+                    postprocessing_settings=conditioning_data.postprocessing_settings,
+                    latents=latents,
+                    sigma=batched_t,
+                    step_index=i,
+                    total_step_count=len(timesteps),
+                )
+
                 predicted_original = getattr(step_output, "pred_original_sample", None)
 
                 if callback is not None:
@@ -497,15 +485,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         self,
         t: torch.Tensor,
         latents: torch.Tensor,
-        conditioning_data: TextConditioningData,
+        conditioning_data: ConditioningData,
         step_index: int,
         total_step_count: int,
-        scheduler_step_kwargs: dict[str, Any],
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
         ip_adapter_data: Optional[list[IPAdapterData]] = None,
         t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
-        unet_attention_patcher: Optional[UNetAttentionPatcher] = None,
+        ip_adapter_unet_patcher: Optional[UNetPatcher] = None,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
@@ -528,10 +515,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 )
                 if step_index >= first_adapter_step and step_index <= last_adapter_step:
                     # Only apply this IP-Adapter if the current step is within the IP-Adapter's begin/end step range.
-                    unet_attention_patcher.set_scale(i, weight)
+                    ip_adapter_unet_patcher.set_scale(i, weight)
                 else:
                     # Otherwise, set the IP-Adapter's scale to 0, so it has no effect.
-                    unet_attention_patcher.set_scale(i, 0.0)
+                    ip_adapter_unet_patcher.set_scale(i, 0.0)
 
         # Handle ControlNet(s)
         down_block_additional_residuals = None
@@ -575,17 +562,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
             down_intrablock_additional_residuals = accum_adapter_state
 
-        ip_adapter_conditioning = None
-        if ip_adapter_data is not None:
-            ip_adapter_conditioning = [ipa.ip_adapter_conditioning for ipa in ip_adapter_data]
-
         uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
             sample=latent_model_input,
             timestep=t,  # TODO: debug how handled batched and non batched timesteps
             step_index=step_index,
             total_step_count=total_step_count,
             conditioning_data=conditioning_data,
-            ip_adapter_conditioning=ip_adapter_conditioning,
             down_block_additional_residuals=down_block_additional_residuals,  # for ControlNet
             mid_block_additional_residual=mid_block_additional_residual,  # for ControlNet
             down_intrablock_additional_residuals=down_intrablock_additional_residuals,  # for T2I-Adapter
@@ -605,7 +587,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             )
 
         # compute the previous noisy sample x_t -> x_t-1
-        step_output = self.scheduler.step(noise_pred, timestep, latents, **scheduler_step_kwargs)
+        step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
 
         # TODO: issue to diffusers?
         # undo internal counter increment done by scheduler.step, so timestep can be resolved as before call

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -1,5 +1,7 @@
-from dataclasses import dataclass
-from typing import List, Optional, Union
+import dataclasses
+import inspect
+from dataclasses import dataclass, field
+from typing import Any, List, Optional, Union
 
 import torch
 
@@ -8,11 +10,6 @@ from .cross_attention_control import Arguments
 
 @dataclass
 class ExtraConditioningInfo:
-    """Extra conditioning information produced by Compel.
-
-    This is used for prompt-to-prompt cross-attention control (a.k.a. `.swap()` in Compel).
-    """
-
     tokens_count_including_eos_bos: int
     cross_attention_control_args: Optional[Arguments] = None
 
@@ -23,8 +20,6 @@ class ExtraConditioningInfo:
 
 @dataclass
 class BasicConditioningInfo:
-    """SD 1/2 text conditioning information produced by Compel."""
-
     embeds: torch.Tensor
     extra_conditioning: Optional[ExtraConditioningInfo]
 
@@ -40,8 +35,6 @@ class ConditioningFieldData:
 
 @dataclass
 class SDXLConditioningInfo(BasicConditioningInfo):
-    """SDXL text conditioning information produced by Compel."""
-
     pooled_embeds: torch.Tensor
     add_time_ids: torch.Tensor
 
@@ -51,6 +44,14 @@ class SDXLConditioningInfo(BasicConditioningInfo):
         return super().to(device=device, dtype=dtype)
 
 
+@dataclass(frozen=True)
+class PostprocessingSettings:
+    threshold: float
+    warmup: float
+    h_symmetry_time_pct: Optional[float]
+    v_symmetry_time_pct: Optional[float]
+
+
 @dataclass
 class IPAdapterConditioningInfo:
     cond_image_prompt_embeds: torch.Tensor
@@ -64,55 +65,41 @@ class IPAdapterConditioningInfo:
 
 
 @dataclass
-class Range:
-    start: int
-    end: int
+class ConditioningData:
+    unconditioned_embeddings: BasicConditioningInfo
+    text_embeddings: BasicConditioningInfo
+    """
+    Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+    `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
+    Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
+    images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
+    """
+    guidance_scale: Union[float, List[float]]
+    """ for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
+     ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
+    """
+    guidance_rescale_multiplier: float = 0
+    scheduler_args: dict[str, Any] = field(default_factory=dict)
+    """
+    Additional arguments to pass to invokeai_diffuser.do_latent_postprocessing().
+    """
+    postprocessing_settings: Optional[PostprocessingSettings] = None
 
+    ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]] = None
 
-class TextConditioningRegions:
-    def __init__(
-        self,
-        masks: torch.Tensor,
-        ranges: list[Range],
-        mask_weights: list[float],
-    ):
-        # A binary mask indicating the regions of the image that the prompt should be applied to.
-        # Shape: (1, num_prompts, height, width)
-        # Dtype: torch.bool
-        self.masks = masks
+    @property
+    def dtype(self):
+        return self.text_embeddings.dtype
 
-        # A list of ranges indicating the start and end indices of the embeddings that corresponding mask applies to.
-        # ranges[i] contains the embedding range for the i'th prompt / mask.
-        self.ranges = ranges
-
-        self.mask_weights = mask_weights
-
-        assert self.masks.shape[1] == len(self.ranges) == len(self.mask_weights)
-
-
-class TextConditioningData:
-    def __init__(
-        self,
-        uncond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
-        cond_text: Union[BasicConditioningInfo, SDXLConditioningInfo],
-        uncond_regions: Optional[TextConditioningRegions],
-        cond_regions: Optional[TextConditioningRegions],
-        guidance_scale: Union[float, List[float]],
-        guidance_rescale_multiplier: float = 0,
-    ):
-        self.uncond_text = uncond_text
-        self.cond_text = cond_text
-        self.uncond_regions = uncond_regions
-        self.cond_regions = cond_regions
-        # Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-        # `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
-        # Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
-        # images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
-        self.guidance_scale = guidance_scale
-        # For models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7.
-        # See [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
-        self.guidance_rescale_multiplier = guidance_rescale_multiplier
-
-    def is_sdxl(self):
-        assert isinstance(self.uncond_text, SDXLConditioningInfo) == isinstance(self.cond_text, SDXLConditioningInfo)
-        return isinstance(self.cond_text, SDXLConditioningInfo)
+    def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
+        scheduler_args = dict(self.scheduler_args)
+        step_method = inspect.signature(scheduler.step)
+        for name, value in kwargs.items():
+            try:
+                step_method.bind_partial(**{name: value})
+            except TypeError:
+                # FIXME: don't silently discard arguments
+                pass  # debug("%s does not accept argument named %r", scheduler, name)
+            else:
+                scheduler_args[name] = value
+        return dataclasses.replace(self, scheduler_args=scheduler_args)

invokeai/backend/stable_diffusion/diffusion/custom_attention.py

@@ -1,242 +0,0 @@
-import math
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from diffusers.models.attention_processor import Attention, AttnProcessor2_0
-from diffusers.utils import USE_PEFT_BACKEND
-
-from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionProcessorWeights
-from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
-
-
-class CustomAttnProcessor2_0(AttnProcessor2_0):
-    """A custom implementation of AttnProcessor2_0 that supports additional Invoke features.
-
-    This implementation is based on
-    https://github.com/huggingface/diffusers/blame/fcfa270fbd1dc294e2f3a505bae6bcb791d721c3/src/diffusers/models/attention_processor.py#L1204
-
-    Supported custom features:
-    - IP-Adapter
-    - Regional prompt attention
-    """
-
-    def __init__(
-        self,
-        ip_adapter_weights: Optional[list[IPAttentionProcessorWeights]] = None,
-        ip_adapter_scales: Optional[list[float]] = None,
-    ):
-        """Initialize a CustomAttnProcessor2_0.
-
-        Note: Arguments that are the same for all attention layers are passed to __call__(). Arguments that are
-        layer-specific are passed to __init__().
-
-        Args:
-            ip_adapter_weights: The IP-Adapter attention weights. ip_adapter_weights[i] contains the attention weights
-                for the i'th IP-Adapter.
-            ip_adapter_scales: The IP-Adapter attention scales. ip_adapter_scales[i] contains the attention scale for
-                the i'th IP-Adapter.
-        """
-        super().__init__()
-
-        self._ip_adapter_weights = ip_adapter_weights
-        self._ip_adapter_scales = ip_adapter_scales
-
-        assert (self._ip_adapter_weights is None) == (self._ip_adapter_scales is None)
-        if self._ip_adapter_weights is not None:
-            assert len(ip_adapter_weights) == len(ip_adapter_scales)
-
-    def _is_ip_adapter_enabled(self) -> bool:
-        return self._ip_adapter_weights is not None
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        temb: Optional[torch.FloatTensor] = None,
-        scale: float = 1.0,
-        # For regional prompting:
-        regional_prompt_data: Optional[RegionalPromptData] = None,
-        percent_through: Optional[float] = None,
-        # For IP-Adapter:
-        ip_adapter_image_prompt_embeds: Optional[list[torch.Tensor]] = None,
-    ) -> torch.FloatTensor:
-        """Apply attention.
-
-        Args:
-            regional_prompt_data: The regional prompt data for the current batch. If not None, this will be used to
-                apply regional prompt masking.
-            ip_adapter_image_prompt_embeds: The IP-Adapter image prompt embeddings for the current batch.
-                ip_adapter_image_prompt_embeds[i] contains the image prompt embeddings for the i'th IP-Adapter. Each
-                tensor has shape (batch_size, num_ip_images, seq_len, ip_embedding_len).
-        """
-        # If true, we are doing cross-attention, if false we are doing self-attention.
-        is_cross_attention = encoder_hidden_states is not None
-
-        # Start unmodified block from AttnProcessor2_0.
-        # vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
-        residual = hidden_states
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-        # End unmodified block from AttnProcessor2_0.
-
-        # Handle regional prompt attention masks.
-        if regional_prompt_data is not None:
-            assert percent_through is not None
-            _, query_seq_len, _ = hidden_states.shape
-            if is_cross_attention:
-                prompt_region_attention_mask = regional_prompt_data.get_cross_attn_mask(
-                    query_seq_len=query_seq_len, key_seq_len=sequence_length
-                )
-                # TODO(ryand): Avoid redundant type/device conversion here.
-                prompt_region_attention_mask = prompt_region_attention_mask.to(
-                    dtype=hidden_states.dtype, device=hidden_states.device
-                )
-
-                attn_mask_weight = 1.0 * ((1 - percent_through) ** 5)
-            else:  # self-attention
-                prompt_region_attention_mask = regional_prompt_data.get_self_attn_mask(
-                    query_seq_len=query_seq_len,
-                    percent_through=percent_through,
-                )
-                attn_mask_weight = 0.3 * ((1 - percent_through) ** 5)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        args = () if USE_PEFT_BACKEND else (scale,)
-        query = attn.to_q(hidden_states, *args)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states, *args)
-        value = attn.to_v(encoder_hidden_states, *args)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if regional_prompt_data is not None and percent_through < 0.3:
-            # Don't apply to uncond????
-
-            prompt_region_attention_mask = attn.prepare_attention_mask(
-                prompt_region_attention_mask, sequence_length, batch_size
-            )
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            prompt_region_attention_mask = prompt_region_attention_mask.view(
-                batch_size, attn.heads, -1, prompt_region_attention_mask.shape[-1]
-            )
-
-            scale_factor = 1 / math.sqrt(query.size(-1))
-            attn_weight = query @ key.transpose(-2, -1) * scale_factor
-            m_pos = attn_weight.max(dim=-1, keepdim=True)[0] - attn_weight
-            m_neg = attn_weight - attn_weight.min(dim=-1, keepdim=True)[0]
-
-            prompt_region_attention_mask = attn_mask_weight * (
-                m_pos * prompt_region_attention_mask - m_neg * (1.0 - prompt_region_attention_mask)
-            )
-
-            if attention_mask is None:
-                attention_mask = prompt_region_attention_mask
-            else:
-                attention_mask = prompt_region_attention_mask + attention_mask
-        else:
-            pass
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-        # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-        # End unmodified block from AttnProcessor2_0.
-
-        # Apply IP-Adapter conditioning.
-        if is_cross_attention and self._is_ip_adapter_enabled():
-            if self._is_ip_adapter_enabled():
-                assert ip_adapter_image_prompt_embeds is not None
-                for ipa_embed, ipa_weights, scale in zip(
-                    ip_adapter_image_prompt_embeds, self._ip_adapter_weights, self._ip_adapter_scales, strict=True
-                ):
-                    # The batch dimensions should match.
-                    assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
-                    # The token_len dimensions should match.
-                    assert ipa_embed.shape[-1] == encoder_hidden_states.shape[-1]
-
-                    ip_hidden_states = ipa_embed
-
-                    # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
-
-                    ip_key = ipa_weights.to_k_ip(ip_hidden_states)
-                    ip_value = ipa_weights.to_v_ip(ip_hidden_states)
-
-                    # Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
-
-                    ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-                    ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-                    # Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
-
-                    # TODO: add support for attn.scale when we move to Torch 2.1
-                    ip_hidden_states = F.scaled_dot_product_attention(
-                        query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
-                    )
-
-                    # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
-
-                    ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-                    ip_hidden_states = ip_hidden_states.to(query.dtype)
-
-                    # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
-
-                    hidden_states = hidden_states + scale * ip_hidden_states
-            else:
-                # If IP-Adapter is not enabled, then ip_adapter_image_prompt_embeds should not be passed in.
-                assert ip_adapter_image_prompt_embeds is None
-
-        # Start unmodified block from AttnProcessor2_0.
-        # vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states, *args)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states

invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py

@@ -1,164 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    TextConditioningRegions,
-)
-
-
-class RegionalPromptData:
-    def __init__(
-        self,
-        regions: list[TextConditioningRegions],
-        device: torch.device,
-        dtype: torch.dtype,
-        max_downscale_factor: int = 8,
-    ):
-        """Initialize a `RegionalPromptData` object.
-
-        Args:
-            regions (list[TextConditioningRegions]): regions[i] contains the prompt regions for the i'th sample in the
-                batch.
-            device (torch.device): The device to use for the attention masks.
-            dtype (torch.dtype): The data type to use for the attention masks.
-            max_downscale_factor: Spatial masks will be prepared for downscale factors from 1 to max_downscale_factor
-                in steps of 2x.
-        """
-        self._regions = regions
-        self._device = device
-        self._dtype = dtype
-        # self._spatial_masks_by_seq_len[b][s] contains the spatial masks for the b'th batch sample with a query
-        # sequence length of s.
-        self._spatial_masks_by_seq_len: list[dict[int, torch.Tensor]] = self._prepare_spatial_masks(
-            regions, max_downscale_factor
-        )
-        self._negative_cross_attn_mask_score = 0.0
-        self._size_weight = 1.0
-
-    def _prepare_spatial_masks(
-        self, regions: list[TextConditioningRegions], max_downscale_factor: int = 8
-    ) -> list[dict[int, torch.Tensor]]:
-        """Prepare the spatial masks for all downscaling factors."""
-        # batch_masks_by_seq_len[b][s] contains the spatial masks for the b'th batch sample with a query sequence length
-        # of s.
-        batch_sample_masks_by_seq_len: list[dict[int, torch.Tensor]] = []
-
-        for batch_sample_regions in regions:
-            batch_sample_masks_by_seq_len.append({})
-
-            # Convert the bool masks to float masks so that max pooling can be applied.
-            batch_sample_masks = batch_sample_regions.masks.to(device=self._device, dtype=self._dtype)
-
-            # Downsample the spatial dimensions by factors of 2 until max_downscale_factor is reached.
-            downscale_factor = 1
-            while downscale_factor <= max_downscale_factor:
-                b, _num_prompts, h, w = batch_sample_masks.shape
-                assert b == 1
-                query_seq_len = h * w
-
-                batch_sample_masks_by_seq_len[-1][query_seq_len] = batch_sample_masks
-
-                downscale_factor *= 2
-                if downscale_factor <= max_downscale_factor:
-                    # We use max pooling because we downscale to a pretty low resolution, so we don't want small prompt
-                    # regions to be lost entirely.
-                    # TODO(ryand): In the future, we may want to experiment with other downsampling methods, and could
-                    # potentially use a weighted mask rather than a binary mask.
-                    batch_sample_masks = F.max_pool2d(batch_sample_masks, kernel_size=2, stride=2)
-
-        return batch_sample_masks_by_seq_len
-
-    def get_cross_attn_mask(self, query_seq_len: int, key_seq_len: int) -> torch.Tensor:
-        """Get the cross-attention mask for the given query sequence length.
-
-        Args:
-            query_seq_len: The length of the flattened spatial features at the current downscaling level.
-            key_seq_len (int): The sequence length of the prompt embeddings (which act as the key in the cross-attention
-                layers). This is most likely equal to the max embedding range end, but we pass it explicitly to be sure.
-
-        Returns:
-            torch.Tensor: The masks.
-                shape: (batch_size, query_seq_len, key_seq_len).
-                dtype: float
-                The mask is a binary mask with values of 0.0 and 1.0.
-        """
-        batch_size = len(self._spatial_masks_by_seq_len)
-        batch_spatial_masks = [self._spatial_masks_by_seq_len[b][query_seq_len] for b in range(batch_size)]
-
-        # Create an empty attention mask with the correct shape.
-        attn_mask = torch.zeros((batch_size, query_seq_len, key_seq_len), dtype=self._dtype, device=self._device)
-
-        for batch_idx in range(batch_size):
-            batch_sample_spatial_masks = batch_spatial_masks[batch_idx]
-            batch_sample_regions = self._regions[batch_idx]
-
-            # Flatten the spatial dimensions of the mask by reshaping to (1, num_prompts, query_seq_len, 1).
-            _, num_prompts, _, _ = batch_sample_spatial_masks.shape
-            batch_sample_query_masks = batch_sample_spatial_masks.view((1, num_prompts, query_seq_len, 1))
-
-            for prompt_idx, embedding_range in enumerate(batch_sample_regions.ranges):
-                batch_sample_query_scores = batch_sample_query_masks[0, prompt_idx, :, :]
-                size = batch_sample_query_scores.sum() / batch_sample_query_scores.numel()
-                mask_weight = batch_sample_regions.mask_weights[prompt_idx]
-                # size = size.to(dtype=batch_sample_query_scores.dtype)
-                # batch_sample_query_mask = batch_sample_query_scores > 0.5
-                # batch_sample_query_scores[batch_sample_query_mask] = 1.0 * (1.0 - size)
-                # batch_sample_query_scores[~batch_sample_query_mask] = 0.0
-                attn_mask[batch_idx, :, embedding_range.start : embedding_range.end] = batch_sample_query_scores * (
-                    mask_weight + self._size_weight * (1 - size)
-                )
-
-        return attn_mask
-
-    def get_self_attn_mask(self, query_seq_len: int, percent_through: float) -> torch.Tensor:
-        """Get the self-attention mask for the given query sequence length.
-
-        Args:
-            query_seq_len: The length of the flattened spatial features at the current downscaling level.
-
-        Returns:
-            torch.Tensor: The masks.
-                shape: (batch_size, query_seq_len, query_seq_len).
-                dtype: float
-                The mask is a binary mask with values of 0.0 and 1.0.
-        """
-        batch_size = len(self._spatial_masks_by_seq_len)
-        batch_spatial_masks = [self._spatial_masks_by_seq_len[b][query_seq_len] for b in range(batch_size)]
-
-        # Create an empty attention mask with the correct shape.
-        attn_mask = torch.zeros((batch_size, query_seq_len, query_seq_len), dtype=self._dtype, device=self._device)
-
-        for batch_idx in range(batch_size):
-            batch_sample_spatial_masks = batch_spatial_masks[batch_idx]
-            batch_sample_regions = self._regions[batch_idx]
-
-            # Flatten the spatial dimensions of the mask by reshaping to (1, num_prompts, query_seq_len, 1).
-            _, num_prompts, _, _ = batch_sample_spatial_masks.shape
-            batch_sample_query_masks = batch_sample_spatial_masks.view((1, num_prompts, query_seq_len, 1))
-
-            for prompt_idx in range(num_prompts):
-                prompt_query_mask = batch_sample_query_masks[0, prompt_idx, :, 0]  # Shape: (query_seq_len,)
-                size = prompt_query_mask.sum() / prompt_query_mask.numel()
-                size = size.to(dtype=prompt_query_mask.dtype)
-                mask_weight = batch_sample_regions.mask_weights[prompt_idx]
-                # Multiply a (1, query_seq_len) mask by a (query_seq_len, 1) mask to get a (query_seq_len,
-                # query_seq_len) mask.
-                # TODO(ryand): Is += really the best option here? Maybe elementwise max is better?
-                attn_mask[batch_idx, :, :] = torch.maximum(
-                    attn_mask[batch_idx, :, :],
-                    prompt_query_mask.unsqueeze(0)
-                    * prompt_query_mask.unsqueeze(1)
-                    * (mask_weight + self._size_weight * (1 - size)),
-                )
-
-            # if attn_mask[batch_idx].max() < 0.01:
-            #     attn_mask[batch_idx, ...] = 1.0
-
-        # attn_mask[attn_mask > 0.5] = 1.0
-        # attn_mask[attn_mask <= 0.5] = 0.0
-        # attn_mask_min = attn_mask[batch_idx].min()
-
-        # # Adjust so that the minimum value is 0.0 regardless of whether all pixels are covered or not.
-        # if abs(attn_mask_min) > 0.0001:
-        #     attn_mask[batch_idx] = attn_mask[batch_idx] - attn_mask_min
-        return attn_mask

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import math
-import time
 from contextlib import contextmanager
 from typing import Any, Callable, Optional, Union
 
@@ -11,13 +10,11 @@ from typing_extensions import TypeAlias
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+    ConditioningData,
     ExtraConditioningInfo,
-    IPAdapterConditioningInfo,
-    Range,
-    TextConditioningData,
-    TextConditioningRegions,
+    PostprocessingSettings,
+    SDXLConditioningInfo,
 )
-from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
 
 from .cross_attention_control import (
     CrossAttentionType,
@@ -59,6 +56,7 @@ class InvokeAIDiffuserComponent:
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
         """
         config = InvokeAIAppConfig.get_config()
+        self.conditioning = None
         self.model = model
         self.model_forward_callback = model_forward_callback
         self.cross_attention_control_context = None
@@ -93,7 +91,7 @@ class InvokeAIDiffuserComponent:
         timestep: torch.Tensor,
         step_index: int,
         total_step_count: int,
-        conditioning_data: TextConditioningData,
+        conditioning_data,
     ):
         down_block_res_samples, mid_block_res_sample = None, None
 
@@ -126,30 +124,38 @@ class InvokeAIDiffuserComponent:
                 added_cond_kwargs = None
 
                 if cfg_injection:  # only applying ControlNet to conditional instead of in unconditioned
-                    if conditioning_data.is_sdxl():
+                    if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
                         added_cond_kwargs = {
-                            "text_embeds": conditioning_data.cond_text.pooled_embeds,
-                            "time_ids": conditioning_data.cond_text.add_time_ids,
+                            "text_embeds": conditioning_data.text_embeddings.pooled_embeds,
+                            "time_ids": conditioning_data.text_embeddings.add_time_ids,
                         }
-                    encoder_hidden_states = conditioning_data.cond_text.embeds
+                    encoder_hidden_states = conditioning_data.text_embeddings.embeds
                     encoder_attention_mask = None
                 else:
-                    if conditioning_data.is_sdxl():
+                    if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
                         added_cond_kwargs = {
                             "text_embeds": torch.cat(
                                 [
-                                    conditioning_data.uncond_text.pooled_embeds,
-                                    conditioning_data.cond_text.pooled_embeds,
+                                    # TODO: how to pad? just by zeros? or even truncate?
+                                    conditioning_data.unconditioned_embeddings.pooled_embeds,
+                                    conditioning_data.text_embeddings.pooled_embeds,
                                 ],
                                 dim=0,
                             ),
                             "time_ids": torch.cat(
-                                [conditioning_data.uncond_text.add_time_ids, conditioning_data.cond_text.add_time_ids],
+                                [
+                                    conditioning_data.unconditioned_embeddings.add_time_ids,
+                                    conditioning_data.text_embeddings.add_time_ids,
+                                ],
                                 dim=0,
                             ),
                         }
-                    (encoder_hidden_states, encoder_attention_mask) = self._concat_conditionings_for_batch(
-                        conditioning_data.uncond_text.embeds, conditioning_data.cond_text.embeds
+                    (
+                        encoder_hidden_states,
+                        encoder_attention_mask,
+                    ) = self._concat_conditionings_for_batch(
+                        conditioning_data.unconditioned_embeddings.embeds,
+                        conditioning_data.text_embeddings.embeds,
                     )
                 if isinstance(control_datum.weight, list):
                     # if controlnet has multiple weights, use the weight for the current step
@@ -193,17 +199,16 @@ class InvokeAIDiffuserComponent:
         self,
         sample: torch.Tensor,
         timestep: torch.Tensor,
-        conditioning_data: TextConditioningData,
-        ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]],
+        conditioning_data: ConditioningData,
         step_index: int,
         total_step_count: int,
         down_block_additional_residuals: Optional[torch.Tensor] = None,  # for ControlNet
         mid_block_additional_residual: Optional[torch.Tensor] = None,  # for ControlNet
         down_intrablock_additional_residuals: Optional[torch.Tensor] = None,  # for T2I-Adapter
     ):
-        percent_through = step_index / total_step_count
         cross_attention_control_types_to_do = []
         if self.cross_attention_control_context is not None:
+            percent_through = step_index / total_step_count
             cross_attention_control_types_to_do = (
                 self.cross_attention_control_context.get_active_cross_attention_control_types_for_step(percent_through)
             )
@@ -219,8 +224,6 @@ class InvokeAIDiffuserComponent:
                 x=sample,
                 sigma=timestep,
                 conditioning_data=conditioning_data,
-                ip_adapter_conditioning=ip_adapter_conditioning,
-                percent_through=percent_through,
                 cross_attention_control_types_to_do=cross_attention_control_types_to_do,
                 down_block_additional_residuals=down_block_additional_residuals,
                 mid_block_additional_residual=mid_block_additional_residual,
@@ -234,8 +237,6 @@ class InvokeAIDiffuserComponent:
                 x=sample,
                 sigma=timestep,
                 conditioning_data=conditioning_data,
-                percent_through=percent_through,
-                ip_adapter_conditioning=ip_adapter_conditioning,
                 down_block_additional_residuals=down_block_additional_residuals,
                 mid_block_additional_residual=mid_block_additional_residual,
                 down_intrablock_additional_residuals=down_intrablock_additional_residuals,
@@ -243,6 +244,19 @@ class InvokeAIDiffuserComponent:
 
         return unconditioned_next_x, conditioned_next_x
 
+    def do_latent_postprocessing(
+        self,
+        postprocessing_settings: PostprocessingSettings,
+        latents: torch.Tensor,
+        sigma,
+        step_index,
+        total_step_count,
+    ) -> torch.Tensor:
+        if postprocessing_settings is not None:
+            percent_through = step_index / total_step_count
+            latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
+        return latents
+
     def _concat_conditionings_for_batch(self, unconditioning, conditioning):
         def _pad_conditioning(cond, target_len, encoder_attention_mask):
             conditioning_attention_mask = torch.ones(
@@ -290,13 +304,13 @@ class InvokeAIDiffuserComponent:
 
         return torch.cat([unconditioning, conditioning]), encoder_attention_mask
 
+    # methods below are called from do_diffusion_step and should be considered private to this class.
+
     def _apply_standard_conditioning(
         self,
         x,
         sigma,
-        conditioning_data: TextConditioningData,
-        ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]],
-        percent_through: float,
+        conditioning_data: ConditioningData,
         down_block_additional_residuals: Optional[torch.Tensor] = None,  # for ControlNet
         mid_block_additional_residual: Optional[torch.Tensor] = None,  # for ControlNet
         down_intrablock_additional_residuals: Optional[torch.Tensor] = None,  # for T2I-Adapter
@@ -307,55 +321,41 @@ class InvokeAIDiffuserComponent:
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
 
-        cross_attention_kwargs = {}
-        if ip_adapter_conditioning is not None:
+        cross_attention_kwargs = None
+        if conditioning_data.ip_adapter_conditioning is not None:
             # Note that we 'stack' to produce tensors of shape (batch_size, num_ip_images, seq_len, token_len).
-            cross_attention_kwargs["ip_adapter_image_prompt_embeds"] = [
-                torch.stack([ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds])
-                for ipa_conditioning in ip_adapter_conditioning
-            ]
-
-        uncond_text = conditioning_data.uncond_text
-        cond_text = conditioning_data.cond_text
+            cross_attention_kwargs = {
+                "ip_adapter_image_prompt_embeds": [
+                    torch.stack(
+                        [ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds]
+                    )
+                    for ipa_conditioning in conditioning_data.ip_adapter_conditioning
+                ]
+            }
 
         added_cond_kwargs = None
-        if conditioning_data.is_sdxl():
+        if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
             added_cond_kwargs = {
-                "text_embeds": torch.cat([uncond_text.pooled_embeds, cond_text.pooled_embeds], dim=0),
-                "time_ids": torch.cat([uncond_text.add_time_ids, cond_text.add_time_ids], dim=0),
+                "text_embeds": torch.cat(
+                    [
+                        # TODO: how to pad? just by zeros? or even truncate?
+                        conditioning_data.unconditioned_embeddings.pooled_embeds,
+                        conditioning_data.text_embeddings.pooled_embeds,
+                    ],
+                    dim=0,
+                ),
+                "time_ids": torch.cat(
+                    [
+                        conditioning_data.unconditioned_embeddings.add_time_ids,
+                        conditioning_data.text_embeddings.add_time_ids,
+                    ],
+                    dim=0,
+                ),
             }
 
         both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
-            uncond_text.embeds, cond_text.embeds
+            conditioning_data.unconditioned_embeddings.embeds, conditioning_data.text_embeddings.embeds
         )
-
-        if conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None:
-            # TODO(ryand): We currently initialize RegionalPromptData for every denoising step. The text conditionings
-            # and masks are not changing from step-to-step, so this really only needs to be done once. While this seems
-            # painfully inefficient, the time spent is typically negligible compared to the forward inference pass of
-            # the UNet. The main reason that this hasn't been moved up to eliminate redundancy is that it is slightly
-            # awkward to handle both standard conditioning and sequential conditioning further up the stack.
-            regions = []
-            for c, r in [
-                (conditioning_data.uncond_text, conditioning_data.uncond_regions),
-                (conditioning_data.cond_text, conditioning_data.cond_regions),
-            ]:
-                if r is None:
-                    # Create a dummy mask and range for text conditioning that doesn't have region masks.
-                    _, _, h, w = x.shape
-                    r = TextConditioningRegions(
-                        masks=torch.ones((1, 1, h, w), dtype=torch.bool),
-                        ranges=[Range(start=0, end=c.embeds.shape[1])],
-                        mask_weights=[0.0],
-                    )
-                regions.append(r)
-
-            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
-                regions=regions, device=x.device, dtype=x.dtype
-            )
-            cross_attention_kwargs["percent_through"] = percent_through
-            time.sleep(1.0)
-
         both_results = self.model_forward_callback(
             x_twice,
             sigma_twice,
@@ -374,10 +374,8 @@ class InvokeAIDiffuserComponent:
         self,
         x: torch.Tensor,
         sigma,
-        conditioning_data: TextConditioningData,
-        ip_adapter_conditioning: Optional[list[IPAdapterConditioningInfo]],
+        conditioning_data: ConditioningData,
         cross_attention_control_types_to_do: list[CrossAttentionType],
-        percent_through: float,
         down_block_additional_residuals: Optional[torch.Tensor] = None,  # for ControlNet
         mid_block_additional_residual: Optional[torch.Tensor] = None,  # for ControlNet
         down_intrablock_additional_residuals: Optional[torch.Tensor] = None,  # for T2I-Adapter
@@ -424,40 +422,36 @@ class InvokeAIDiffuserComponent:
         # Unconditioned pass
         #####################
 
-        cross_attention_kwargs = {}
+        cross_attention_kwargs = None
 
         # Prepare IP-Adapter cross-attention kwargs for the unconditioned pass.
-        if ip_adapter_conditioning is not None:
+        if conditioning_data.ip_adapter_conditioning is not None:
             # Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len).
-            cross_attention_kwargs["ip_adapter_image_prompt_embeds"] = [
-                torch.unsqueeze(ipa_conditioning.uncond_image_prompt_embeds, dim=0)
-                for ipa_conditioning in ip_adapter_conditioning
-            ]
+            cross_attention_kwargs = {
+                "ip_adapter_image_prompt_embeds": [
+                    torch.unsqueeze(ipa_conditioning.uncond_image_prompt_embeds, dim=0)
+                    for ipa_conditioning in conditioning_data.ip_adapter_conditioning
+                ]
+            }
 
         # Prepare cross-attention control kwargs for the unconditioned pass.
         if cross_attn_processor_context is not None:
-            cross_attention_kwargs["swap_cross_attn_context"] = cross_attn_processor_context
+            cross_attention_kwargs = {"swap_cross_attn_context": cross_attn_processor_context}
 
         # Prepare SDXL conditioning kwargs for the unconditioned pass.
         added_cond_kwargs = None
-        if conditioning_data.is_sdxl():
+        is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo
+        if is_sdxl:
             added_cond_kwargs = {
-                "text_embeds": conditioning_data.uncond_text.pooled_embeds,
-                "time_ids": conditioning_data.uncond_text.add_time_ids,
+                "text_embeds": conditioning_data.unconditioned_embeddings.pooled_embeds,
+                "time_ids": conditioning_data.unconditioned_embeddings.add_time_ids,
             }
 
-        # Prepare prompt regions for the unconditioned pass.
-        if conditioning_data.uncond_regions is not None:
-            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
-                regions=[conditioning_data.uncond_regions], device=x.device, dtype=x.dtype
-            )
-            cross_attention_kwargs["percent_through"] = percent_through
-
         # Run unconditioned UNet denoising (i.e. negative prompt).
         unconditioned_next_x = self.model_forward_callback(
             x,
             sigma,
-            conditioning_data.uncond_text.embeds,
+            conditioning_data.unconditioned_embeddings.embeds,
             cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=uncond_down_block,
             mid_block_additional_residual=uncond_mid_block,
@@ -469,41 +463,36 @@ class InvokeAIDiffuserComponent:
         # Conditioned pass
         ###################
 
-        cross_attention_kwargs = {}
+        cross_attention_kwargs = None
 
         # Prepare IP-Adapter cross-attention kwargs for the conditioned pass.
-        if ip_adapter_conditioning is not None:
+        if conditioning_data.ip_adapter_conditioning is not None:
             # Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len).
-            cross_attention_kwargs["ip_adapter_image_prompt_embeds"] = [
-                torch.unsqueeze(ipa_conditioning.cond_image_prompt_embeds, dim=0)
-                for ipa_conditioning in ip_adapter_conditioning
-            ]
+            cross_attention_kwargs = {
+                "ip_adapter_image_prompt_embeds": [
+                    torch.unsqueeze(ipa_conditioning.cond_image_prompt_embeds, dim=0)
+                    for ipa_conditioning in conditioning_data.ip_adapter_conditioning
+                ]
+            }
 
         # Prepare cross-attention control kwargs for the conditioned pass.
         if cross_attn_processor_context is not None:
             cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
-            cross_attention_kwargs["swap_cross_attn_context"] = cross_attn_processor_context
+            cross_attention_kwargs = {"swap_cross_attn_context": cross_attn_processor_context}
 
         # Prepare SDXL conditioning kwargs for the conditioned pass.
         added_cond_kwargs = None
-        if conditioning_data.is_sdxl():
+        if is_sdxl:
             added_cond_kwargs = {
-                "text_embeds": conditioning_data.cond_text.pooled_embeds,
-                "time_ids": conditioning_data.cond_text.add_time_ids,
+                "text_embeds": conditioning_data.text_embeddings.pooled_embeds,
+                "time_ids": conditioning_data.text_embeddings.add_time_ids,
             }
 
-        # Prepare prompt regions for the conditioned pass.
-        if conditioning_data.cond_regions is not None:
-            cross_attention_kwargs["regional_prompt_data"] = RegionalPromptData(
-                regions=[conditioning_data.cond_regions], device=x.device, dtype=x.dtype
-            )
-            cross_attention_kwargs["percent_through"] = percent_through
-
         # Run conditioned UNet denoising (i.e. positive prompt).
         conditioned_next_x = self.model_forward_callback(
             x,
             sigma,
-            conditioning_data.cond_text.embeds,
+            conditioning_data.text_embeddings.embeds,
             cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=cond_down_block,
             mid_block_additional_residual=cond_mid_block,
@@ -517,3 +506,64 @@ class InvokeAIDiffuserComponent:
         scaled_delta = (conditioned_next_x - unconditioned_next_x) * guidance_scale
         combined_next_x = unconditioned_next_x + scaled_delta
         return combined_next_x
+
+    def apply_symmetry(
+        self,
+        postprocessing_settings: PostprocessingSettings,
+        latents: torch.Tensor,
+        percent_through: float,
+    ) -> torch.Tensor:
+        # Reset our last percent through if this is our first step.
+        if percent_through == 0.0:
+            self.last_percent_through = 0.0
+
+        if postprocessing_settings is None:
+            return latents
+
+        # Check for out of bounds
+        h_symmetry_time_pct = postprocessing_settings.h_symmetry_time_pct
+        if h_symmetry_time_pct is not None and (h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0):
+            h_symmetry_time_pct = None
+
+        v_symmetry_time_pct = postprocessing_settings.v_symmetry_time_pct
+        if v_symmetry_time_pct is not None and (v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0):
+            v_symmetry_time_pct = None
+
+        dev = latents.device.type
+
+        latents.to(device="cpu")
+
+        if (
+            h_symmetry_time_pct is not None
+            and self.last_percent_through < h_symmetry_time_pct
+            and percent_through >= h_symmetry_time_pct
+        ):
+            # Horizontal symmetry occurs on the 3rd dimension of the latent
+            width = latents.shape[3]
+            x_flipped = torch.flip(latents, dims=[3])
+            latents = torch.cat(
+                [
+                    latents[:, :, :, 0 : int(width / 2)],
+                    x_flipped[:, :, :, int(width / 2) : int(width)],
+                ],
+                dim=3,
+            )
+
+        if (
+            v_symmetry_time_pct is not None
+            and self.last_percent_through < v_symmetry_time_pct
+            and percent_through >= v_symmetry_time_pct
+        ):
+            # Vertical symmetry occurs on the 2nd dimension of the latent
+            height = latents.shape[2]
+            y_flipped = torch.flip(latents, dims=[2])
+            latents = torch.cat(
+                [
+                    latents[:, :, 0 : int(height / 2)],
+                    y_flipped[:, :, int(height / 2) : int(height)],
+                ],
+                dim=2,
+            )
+
+        self.last_percent_through = percent_through
+        return latents.to(device=dev)

invokeai/backend/training/textual_inversion_training.py

@@ -858,9 +858,9 @@ def do_textual_inversion_training(
                 # Let's make sure we don't update any embedding weights besides the newly added token
                 index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
                 with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
-                        orig_embeds_params[index_no_updates]
-                    )
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
+                        index_no_updates
+                    ] = orig_embeds_params[index_no_updates]
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

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/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:",

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/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/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/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/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',

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

@@ -1,14 +1,14 @@
 import { Box } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 
-import { ImportModels } from './ImportModels';
+import { InstallModels } from './InstallModels';
 import { Model } from './ModelPanel/Model';
 
 export const ModelPane = () => {
   const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
   return (
     <Box layerStyle="first" p={2} borderRadius="base" w="50%" h="full">
-      {selectedModelKey ? <Model key={selectedModelKey} /> : <ImportModels />}
+      {selectedModelKey ? <Model key={selectedModelKey} /> : <InstallModels />}
     </Box>
   );
 };

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings.tsx

@@ -1,11 +1,12 @@
+import { Text } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import Loading from 'common/components/Loading/Loading';
 import { selectConfigSlice } from 'features/system/store/configSlice';
 import { isNil } from 'lodash-es';
 import { useMemo } from 'react';
-import { useGetModelMetadataQuery } from 'services/api/endpoints/models';
+import { useTranslation } from 'react-i18next';
+import { useGetModelConfigQuery } from 'services/api/endpoints/models';
 
 import { DefaultSettingsForm } from './DefaultSettings/DefaultSettingsForm';
 
@@ -23,8 +24,9 @@ const initialStatesSelector = createMemoizedSelector(selectConfigSlice, (config)
 
 export const DefaultSettings = () => {
   const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
+  const { t } = useTranslation();
 
-  const { data, isLoading } = useGetModelMetadataQuery(selectedModelKey ?? skipToken);
+  const { data, isLoading } = useGetModelConfigQuery(selectedModelKey ?? skipToken);
   const { initialSteps, initialCfg, initialScheduler, initialCfgRescaleMultiplier, initialVaePrecision } =
     useAppSelector(initialStatesSelector);
 
@@ -59,7 +61,7 @@ export const DefaultSettings = () => {
   ]);
 
   if (isLoading) {
-    return <Loading />;
+    return <Text>{t('common.loading')}</Text>;
   }
 
   return <DefaultSettingsForm defaultSettingsDefaults={defaultSettingsDefaults} />;

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/DefaultSettings/DefaultSettingsForm.tsx

@@ -8,7 +8,7 @@ import type { SubmitHandler } from 'react-hook-form';
 import { useForm } from 'react-hook-form';
 import { useTranslation } from 'react-i18next';
 import { IoPencil } from 'react-icons/io5';
-import { useUpdateModelMetadataMutation } from 'services/api/endpoints/models';
+import { useUpdateModelMutation } from 'services/api/endpoints/models';
 
 import { DefaultCfgRescaleMultiplier } from './DefaultCfgRescaleMultiplier';
 import { DefaultCfgScale } from './DefaultCfgScale';
@@ -41,7 +41,7 @@ export const DefaultSettingsForm = ({
   const { t } = useTranslation();
   const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
 
-  const [editModelMetadata, { isLoading }] = useUpdateModelMetadataMutation();
+  const [updateModel, { isLoading }] = useUpdateModelMutation();
 
   const { handleSubmit, control, formState } = useForm<DefaultSettingsFormData>({
     defaultValues: defaultSettingsDefaults,
@@ -62,7 +62,7 @@ export const DefaultSettingsForm = ({
         scheduler: data.scheduler.isEnabled ? data.scheduler.value : null,
       };
 
-      editModelMetadata({
+      updateModel({
         key: selectedModelKey,
         body: { default_settings: body },
       })
@@ -90,7 +90,7 @@ export const DefaultSettingsForm = ({
           }
         });
     },
-    [selectedModelKey, dispatch, editModelMetadata, t]
+    [selectedModelKey, dispatch, updateModel, t]
   );
 
   return (

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/BaseModelSelect.tsx

@@ -3,9 +3,9 @@ import { Combobox } from '@invoke-ai/ui-library';
 import { typedMemo } from 'common/util/typedMemo';
 import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
 import { useCallback, useMemo } from 'react';
-import type { UseControllerProps } from 'react-hook-form';
+import type { Control } from 'react-hook-form';
 import { useController } from 'react-hook-form';
-import type { AnyModelConfig } from 'services/api/types';
+import type { UpdateModelArg } from 'services/api/endpoints/models';
 
 const options: ComboboxOption[] = [
   { value: 'sd-1', label: MODEL_TYPE_MAP['sd-1'] },
@@ -14,8 +14,12 @@ const options: ComboboxOption[] = [
   { value: 'sdxl-refiner', label: MODEL_TYPE_MAP['sdxl-refiner'] },
 ];
 
-const BaseModelSelect = (props: UseControllerProps<AnyModelConfig>) => {
-  const { field } = useController(props);
+type Props = {
+  control: Control<UpdateModelArg['body']>;
+};
+
+const BaseModelSelect = ({ control }: Props) => {
+  const { field } = useController({ control, name: 'base' });
   const value = useMemo(() => options.find((o) => o.value === field.value), [field.value]);
   const onChange = useCallback<ComboboxOnChange>(
     (v) => {

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/BooleanSelect.tsx

@@ -1,27 +0,0 @@
-import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
-import { Combobox } from '@invoke-ai/ui-library';
-import { typedMemo } from 'common/util/typedMemo';
-import { useCallback, useMemo } from 'react';
-import type { UseControllerProps } from 'react-hook-form';
-import { useController } from 'react-hook-form';
-import type { AnyModelConfig } from 'services/api/types';
-
-const options: ComboboxOption[] = [
-  { value: 'none', label: '-' },
-  { value: 'true', label: 'True' },
-  { value: 'false', label: 'False' },
-];
-
-const BooleanSelect = <T extends AnyModelConfig>(props: UseControllerProps<T>) => {
-  const { field } = useController(props);
-  const value = useMemo(() => options.find((o) => o.value === field.value), [field.value]);
-  const onChange = useCallback<ComboboxOnChange>(
-    (v) => {
-      v?.value === 'none' ? field.onChange(undefined) : field.onChange(v?.value === 'true');
-    },
-    [field]
-  );
-  return <Combobox value={value} options={options} onChange={onChange} />;
-};
-
-export default typedMemo(BooleanSelect);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/ModelFormatSelect.tsx

@@ -1,47 +0,0 @@
-import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
-import { Combobox } from '@invoke-ai/ui-library';
-import { typedMemo } from 'common/util/typedMemo';
-import { useCallback, useMemo } from 'react';
-import type { UseControllerProps } from 'react-hook-form';
-import { useController, useWatch } from 'react-hook-form';
-import type { AnyModelConfig } from 'services/api/types';
-
-const ModelFormatSelect = (props: UseControllerProps<AnyModelConfig>) => {
-  const { field, formState } = useController(props);
-  const type = useWatch({ control: props.control, name: 'type' });
-
-  const onChange = useCallback<ComboboxOnChange>(
-    (v) => {
-      field.onChange(v?.value);
-    },
-    [field]
-  );
-
-  const options: ComboboxOption[] = useMemo(() => {
-    const modelType = type || formState.defaultValues?.type;
-    if (modelType === 'lora') {
-      return [
-        { value: 'lycoris', label: 'LyCORIS' },
-        { value: 'diffusers', label: 'Diffusers' },
-      ];
-    } else if (modelType === 'embedding') {
-      return [
-        { value: 'embedding_file', label: 'Embedding File' },
-        { value: 'embedding_folder', label: 'Embedding Folder' },
-      ];
-    } else if (modelType === 'ip_adapter') {
-      return [{ value: 'invokeai', label: 'invokeai' }];
-    } else {
-      return [
-        { value: 'diffusers', label: 'Diffusers' },
-        { value: 'checkpoint', label: 'Checkpoint' },
-      ];
-    }
-  }, [type, formState.defaultValues?.type]);
-
-  const value = useMemo(() => options.find((o) => o.value === field.value), [options, field.value]);
-
-  return <Combobox value={value} options={options} onChange={onChange} />;
-};
-
-export default typedMemo(ModelFormatSelect);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/ModelTypeSelect.tsx

@@ -1,32 +0,0 @@
-import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
-import { Combobox } from '@invoke-ai/ui-library';
-import { typedMemo } from 'common/util/typedMemo';
-import { MODEL_TYPE_LABELS } from 'features/modelManagerV2/subpanels/ModelManagerPanel/ModelTypeFilter';
-import { useCallback, useMemo } from 'react';
-import type { UseControllerProps } from 'react-hook-form';
-import { useController } from 'react-hook-form';
-import type { AnyModelConfig } from 'services/api/types';
-
-const options: ComboboxOption[] = [
-  { value: 'main', label: MODEL_TYPE_LABELS['main'] as string },
-  { value: 'lora', label: MODEL_TYPE_LABELS['lora'] as string },
-  { value: 'embedding', label: MODEL_TYPE_LABELS['embedding'] as string },
-  { value: 'vae', label: MODEL_TYPE_LABELS['vae'] as string },
-  { value: 'controlnet', label: MODEL_TYPE_LABELS['controlnet'] as string },
-  { value: 'ip_adapter', label: MODEL_TYPE_LABELS['ip_adapter'] as string },
-  { value: 't2i_adapater', label: MODEL_TYPE_LABELS['t2i_adapter'] as string },
-] as const;
-
-const ModelTypeSelect = <T extends AnyModelConfig>(props: UseControllerProps<T>) => {
-  const { field } = useController(props);
-  const value = useMemo(() => options.find((o) => o.value === field.value), [field.value]);
-  const onChange = useCallback<ComboboxOnChange>(
-    (v) => {
-      field.onChange(v?.value);
-    },
-    [field]
-  );
-  return <Combobox value={value} options={options} onChange={onChange} />;
-};
-
-export default typedMemo(ModelTypeSelect);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/ModelVariantSelect.tsx

@@ -2,9 +2,9 @@ import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import { Combobox } from '@invoke-ai/ui-library';
 import { typedMemo } from 'common/util/typedMemo';
 import { useCallback, useMemo } from 'react';
-import type { UseControllerProps } from 'react-hook-form';
+import type { Control } from 'react-hook-form';
 import { useController } from 'react-hook-form';
-import type { AnyModelConfig } from 'services/api/types';
+import type { UpdateModelArg } from 'services/api/endpoints/models';
 
 const options: ComboboxOption[] = [
   { value: 'normal', label: 'Normal' },
@@ -12,8 +12,12 @@ const options: ComboboxOption[] = [
   { value: 'depth', label: 'Depth' },
 ];
 
-const ModelVariantSelect = <T extends AnyModelConfig>(props: UseControllerProps<T>) => {
-  const { field } = useController(props);
+type Props = {
+  control: Control<UpdateModelArg['body']>;
+};
+
+const ModelVariantSelect = ({ control }: Props) => {
+  const { field } = useController({ control, name: 'variant' });
   const value = useMemo(() => options.find((o) => o.value === field.value), [field.value]);
   const onChange = useCallback<ComboboxOnChange>(
     (v) => {

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/PredictionTypeSelect.tsx

@@ -2,9 +2,9 @@ import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import { Combobox } from '@invoke-ai/ui-library';
 import { typedMemo } from 'common/util/typedMemo';
 import { useCallback, useMemo } from 'react';
-import type { UseControllerProps } from 'react-hook-form';
+import type { Control } from 'react-hook-form';
 import { useController } from 'react-hook-form';
-import type { AnyModelConfig } from 'services/api/types';
+import type { UpdateModelArg } from 'services/api/endpoints/models';
 
 const options: ComboboxOption[] = [
   { value: 'none', label: '-' },
@@ -13,8 +13,12 @@ const options: ComboboxOption[] = [
   { value: 'sample', label: 'sample' },
 ];
 
-const PredictionTypeSelect = <T extends AnyModelConfig>(props: UseControllerProps<T>) => {
-  const { field } = useController(props);
+type Props = {
+  control: Control<UpdateModelArg['body']>;
+};
+
+const PredictionTypeSelect = ({ control }: Props) => {
+  const { field } = useController({ control, name: 'prediction_type' });
   const value = useMemo(() => options.find((o) => o.value === field.value), [field.value]);
   const onChange = useCallback<ComboboxOnChange>(
     (v) => {

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/RepoVariantSelect.tsx

@@ -1,27 +0,0 @@
-import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
-import { Combobox } from '@invoke-ai/ui-library';
-import { typedMemo } from 'common/util/typedMemo';
-import { useCallback, useMemo } from 'react';
-import type { UseControllerProps } from 'react-hook-form';
-import { useController } from 'react-hook-form';
-import type { AnyModelConfig } from 'services/api/types';
-
-const options: ComboboxOption[] = [
-  { value: 'none', label: '-' },
-  { value: 'fp16', label: 'fp16' },
-  { value: 'fp32', label: 'fp32' },
-];
-
-const RepoVariantSelect = <T extends AnyModelConfig>(props: UseControllerProps<T>) => {
-  const { field } = useController(props);
-  const value = useMemo(() => options.find((o) => o.value === field.value), [field.value]);
-  const onChange = useCallback<ComboboxOnChange>(
-    (v) => {
-      v?.value === 'none' ? field.onChange(undefined) : field.onChange(v?.value);
-    },
-    [field]
-  );
-  return <Combobox value={value} options={options} onChange={onChange} />;
-};
-
-export default typedMemo(RepoVariantSelect);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Metadata/ModelMetadata.tsx

@@ -1,18 +1,41 @@
-import { Flex } from '@invoke-ai/ui-library';
+import { Box, Flex } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppSelector } from 'app/store/storeHooks';
 import DataViewer from 'features/gallery/components/ImageMetadataViewer/DataViewer';
-import { useGetModelMetadataQuery } from 'services/api/endpoints/models';
+import { useMemo } from 'react';
+import { useGetModelConfigQuery } from 'services/api/endpoints/models';
+import type { ModelType } from 'services/api/types';
+
+import { TriggerPhrases } from './TriggerPhrases';
+
+const MODEL_TYPE_TRIGGER_PHRASE: ModelType[] = ['main', 'lora'];
 
 export const ModelMetadata = () => {
   const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
-  const { data: metadata } = useGetModelMetadataQuery(selectedModelKey ?? skipToken);
+  const { data } = useGetModelConfigQuery(selectedModelKey ?? skipToken);
+
+  const shouldShowTriggerPhraseSettings = useMemo(() => {
+    if (!data?.type) {
+      return false;
+    }
+    return MODEL_TYPE_TRIGGER_PHRASE.includes(data.type);
+  }, [data]);
+
+  const apiResponseFormatted = useMemo(() => {
+    if (!data?.source_api_response) {
+      return {};
+    }
+    return JSON.parse(data.source_api_response);
+  }, [data?.source_api_response]);
 
   return (
-    <>
-      <Flex flexDir="column" height="full" gap="3">
-        <DataViewer label="metadata" data={metadata || {}} />
-      </Flex>
-    </>
+    <Flex flexDir="column" height="full" gap="3">
+      {shouldShowTriggerPhraseSettings && (
+        <Box layerStyle="second" borderRadius="base" p={3}>
+          <TriggerPhrases />
+        </Box>
+      )}
+      <DataViewer label="metadata" data={apiResponseFormatted} />
+    </Flex>
   );
 };

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Metadata/TriggerPhrases.tsx

@@ -0,0 +1,106 @@
+import {
+  Button,
+  Flex,
+  FormControl,
+  FormErrorMessage,
+  Input,
+  Tag,
+  TagCloseButton,
+  TagLabel,
+} from '@invoke-ai/ui-library';
+import { skipToken } from '@reduxjs/toolkit/query';
+import { useAppSelector } from 'app/store/storeHooks';
+import { ModelListHeader } from 'features/modelManagerV2/subpanels/ModelManagerPanel/ModelListHeader';
+import type { ChangeEvent } from 'react';
+import { useCallback, useMemo, useState } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useGetModelConfigQuery, useUpdateModelMutation } from 'services/api/endpoints/models';
+
+export const TriggerPhrases = () => {
+  const { t } = useTranslation();
+  const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
+  const { data: modelConfig } = useGetModelConfigQuery(selectedModelKey ?? skipToken);
+  const [phrase, setPhrase] = useState('');
+
+  const [updateModel, { isLoading }] = useUpdateModelMutation();
+
+  const handlePhraseChange = useCallback((e: ChangeEvent<HTMLInputElement>) => {
+    setPhrase(e.target.value);
+  }, []);
+
+  const triggerPhrases = useMemo(() => {
+    return modelConfig?.trigger_phrases || [];
+  }, [modelConfig?.trigger_phrases]);
+
+  const errors = useMemo(() => {
+    const errors = [];
+
+    if (phrase.length && triggerPhrases.includes(phrase)) {
+      errors.push('Phrase is already in list');
+    }
+
+    return errors;
+  }, [phrase, triggerPhrases]);
+
+  const addTriggerPhrase = useCallback(async () => {
+    if (!selectedModelKey) {
+      return;
+    }
+
+    if (!phrase.length || triggerPhrases.includes(phrase)) {
+      return;
+    }
+
+    await updateModel({
+      key: selectedModelKey,
+      body: { trigger_phrases: [...triggerPhrases, phrase] },
+    }).unwrap();
+    setPhrase('');
+  }, [updateModel, selectedModelKey, phrase, triggerPhrases]);
+
+  const removeTriggerPhrase = useCallback(
+    async (phraseToRemove: string) => {
+      if (!selectedModelKey) {
+        return;
+      }
+
+      const filteredPhrases = triggerPhrases.filter((p) => p !== phraseToRemove);
+
+      await updateModel({ key: selectedModelKey, body: { trigger_phrases: filteredPhrases } }).unwrap();
+    },
+    [updateModel, selectedModelKey, triggerPhrases]
+  );
+
+  return (
+    <Flex flexDir="column" w="full" gap="5">
+      <ModelListHeader title={t('modelManager.triggerPhrases')} />
+      <form>
+        <FormControl w="full" isInvalid={Boolean(errors.length)}>
+          <Flex flexDir="column" w="full">
+            <Flex gap="3" alignItems="center" w="full">
+              <Input value={phrase} onChange={handlePhraseChange} placeholder={t('modelManager.typePhraseHere')} />
+              <Button
+                type="submit"
+                onClick={addTriggerPhrase}
+                isDisabled={Boolean(errors.length)}
+                isLoading={isLoading}
+              >
+                {t('common.add')}
+              </Button>
+            </Flex>
+            {!!errors.length && errors.map((error) => <FormErrorMessage key={error}>{error}</FormErrorMessage>)}
+          </Flex>
+        </FormControl>
+      </form>
+
+      <Flex gap="4" flexWrap="wrap" mt="3" mb="3">
+        {triggerPhrases.map((phrase, index) => (
+          <Tag size="md" key={index}>
+            <TagLabel>{phrase}</TagLabel>
+            <TagCloseButton onClick={removeTriggerPhrase.bind(null, phrase)} isDisabled={isLoading} />
+          </Tag>
+        ))}
+      </Flex>
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelConvert.tsx

@@ -13,7 +13,7 @@ import { addToast } from 'features/system/store/systemSlice';
 import { makeToast } from 'features/system/util/makeToast';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useConvertMainModelsMutation } from 'services/api/endpoints/models';
+import { useConvertModelMutation } from 'services/api/endpoints/models';
 import type { CheckpointModelConfig } from 'services/api/types';
 
 interface ModelConvertProps {
@@ -24,7 +24,7 @@ export const ModelConvert = (props: ModelConvertProps) => {
   const { model } = props;
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
-  const [convertModel, { isLoading }] = useConvertMainModelsMutation();
+  const [convertModel, { isLoading }] = useConvertModelMutation();
   const { isOpen, onOpen, onClose } = useDisclosure();
 
   const modelConvertHandler = useCallback(() => {

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelEdit.tsx

@@ -1,5 +1,6 @@
 import {
   Button,
+  Checkbox,
   Flex,
   FormControl,
   FormErrorMessage,
@@ -19,66 +20,27 @@ import type { SubmitHandler } from 'react-hook-form';
 import { useForm } from 'react-hook-form';
 import { useTranslation } from 'react-i18next';
 import type { UpdateModelArg } from 'services/api/endpoints/models';
-import { useGetModelConfigQuery, useUpdateModelsMutation } from 'services/api/endpoints/models';
-import type { AnyModelConfig } from 'services/api/types';
+import { useGetModelConfigQuery, useUpdateModelMutation } from 'services/api/endpoints/models';
 
 import BaseModelSelect from './Fields/BaseModelSelect';
-import BooleanSelect from './Fields/BooleanSelect';
-import ModelFormatSelect from './Fields/ModelFormatSelect';
-import ModelTypeSelect from './Fields/ModelTypeSelect';
 import ModelVariantSelect from './Fields/ModelVariantSelect';
 import PredictionTypeSelect from './Fields/PredictionTypeSelect';
-import RepoVariantSelect from './Fields/RepoVariantSelect';
 
 export const ModelEdit = () => {
   const dispatch = useAppDispatch();
   const selectedModelKey = useAppSelector((s) => s.modelmanagerV2.selectedModelKey);
   const { data, isLoading } = useGetModelConfigQuery(selectedModelKey ?? skipToken);
 
-  const [updateModel, { isLoading: isSubmitting }] = useUpdateModelsMutation();
+  const [updateModel, { isLoading: isSubmitting }] = useUpdateModelMutation();
 
   const { t } = useTranslation();
 
-  // const modelData = useMemo(() => {
-  //   if (!data) {
-  //     return null;
-  //   }
-  //   const modelFormat = data.format;
-  //   const modelType = data.type;
-
-  //   if (modelType === 'main') {
-  //     if (modelFormat === 'diffusers') {
-  //       return data as DiffusersModelConfig;
-  //     } else if (modelFormat === 'checkpoint') {
-  //       return data as CheckpointModelConfig;
-  //     }
-  //   }
-
-  //   switch (modelType) {
-  //     case 'lora':
-  //       return data as LoRAModelConfig;
-  //     case 'embedding':
-  //       return data as TextualInversionModelConfig;
-  //     case 't2i_adapter':
-  //       return data as T2IAdapterModelConfig;
-  //     case 'ip_adapter':
-  //       return data as IPAdapterModelConfig;
-  //     case 'controlnet':
-  //       return data as ControlNetModelConfig;
-  //     case 'vae':
-  //       return data as VAEModelConfig;
-  //     default:
-  //       return null;
-  //   }
-  // }, [data]);
-
   const {
     register,
     handleSubmit,
     control,
     formState: { errors },
     reset,
-    watch,
   } = useForm<UpdateModelArg['body']>({
     defaultValues: {
       ...data,
@@ -86,10 +48,7 @@ export const ModelEdit = () => {
     mode: 'onChange',
   });
 
-  const watchedModelType = watch('type');
-  const watchedModelFormat = watch('format');
-
-  const onSubmit = useCallback<SubmitHandler<AnyModelConfig>>(
+  const onSubmit = useCallback<SubmitHandler<UpdateModelArg['body']>>(
     (values) => {
       if (!data?.key) {
         return;
@@ -143,33 +102,31 @@ export const ModelEdit = () => {
   return (
     <Flex flexDir="column" h="full">
       <form onSubmit={handleSubmit(onSubmit)}>
-        <FormControl flexDir="column" alignItems="flex-start" gap={1} isInvalid={Boolean(errors.name)}>
-          <Flex w="full" justifyContent="space-between" gap={4} alignItems="center">
+        <Flex w="full" justifyContent="space-between" gap={4} alignItems="center">
+          <FormControl flexDir="column" alignItems="flex-start" gap={1} isInvalid={Boolean(errors.name)}>
             <FormLabel hidden={true}>{t('modelManager.modelName')}</FormLabel>
             <Input
               {...register('name', {
-                validate: (value) => value.trim().length > 3 || 'Must be at least 3 characters',
+                validate: (value) => (value && value.trim().length > 3) || 'Must be at least 3 characters',
               })}
               size="lg"
             />
 
-            <Flex gap={2}>
-              <Button size="sm" onClick={handleClickCancel}>
-                {t('common.cancel')}
-              </Button>
-              <Button
-                size="sm"
-                colorScheme="invokeYellow"
-                onClick={handleSubmit(onSubmit)}
-                isLoading={isSubmitting}
-                isDisabled={Boolean(Object.keys(errors).length)}
-              >
-                {t('common.save')}
-              </Button>
-            </Flex>
-          </Flex>
-          {errors.name?.message && <FormErrorMessage>{errors.name?.message}</FormErrorMessage>}
-        </FormControl>
+            {errors.name?.message && <FormErrorMessage>{errors.name?.message}</FormErrorMessage>}
+          </FormControl>
+          <Button size="sm" onClick={handleClickCancel}>
+            {t('common.cancel')}
+          </Button>
+          <Button
+            size="sm"
+            colorScheme="invokeYellow"
+            onClick={handleSubmit(onSubmit)}
+            isLoading={isSubmitting}
+            isDisabled={Boolean(Object.keys(errors).length)}
+          >
+            {t('common.save')}
+          </Button>
+        </Flex>
 
         <Flex flexDir="column" gap={3} mt="4">
           <Flex>
@@ -184,76 +141,22 @@ export const ModelEdit = () => {
           <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('modelManager.modelType')}</FormLabel>
-              <ModelTypeSelect<AnyModelConfig> control={control} name="type" />
+              <BaseModelSelect control={control} />
             </FormControl>
           </Flex>
-          <Flex gap={4}>
-            <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-              <FormLabel>{t('common.format')}</FormLabel>
-              <ModelFormatSelect control={control} name="format" />
-            </FormControl>
-            <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' && (
+          {data.type === 'main' && data.format === 'checkpoint' && (
             <Flex gap={4}>
               <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                <FormLabel>{t('modelManager.imageEncoderModelId')}</FormLabel>
-                <Input {...register('image_encoder_model_id')} />
+                <FormLabel>{t('modelManager.variant')}</FormLabel>
+                <ModelVariantSelect control={control} />
+              </FormControl>
+              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+                <FormLabel>{t('modelManager.predictionType')}</FormLabel>
+                <PredictionTypeSelect control={control} />
+              </FormControl>
+              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+                <FormLabel>{t('modelManager.upcastAttention')}</FormLabel>
+                <Checkbox {...register('upcast_attention')} />
               </FormControl>
             </Flex>
           )}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelView.tsx

@@ -91,26 +91,19 @@ export const ModelView = () => {
             <ModelAttrView label={t('modelManager.path')} value={modelData.path} />
           </Flex>
           {modelData.type === 'main' && (
-            <>
-              <Flex gap={2}>
-                {modelData.format === 'diffusers' && (
-                  <ModelAttrView label={t('modelManager.repoVariant')} value={modelData.repo_variant} />
-                )}
-                {modelData.format === 'checkpoint' && (
-                  <ModelAttrView label={t('modelManager.pathToConfig')} value={modelData.config} />
-                )}
-
-                <ModelAttrView label={t('modelManager.variant')} value={modelData.variant} />
-              </Flex>
-              <Flex gap={2}>
-                <ModelAttrView label={t('modelManager.predictionType')} value={modelData.prediction_type} />
-                <ModelAttrView label={t('modelManager.upcastAttention')} value={`${modelData.upcast_attention}`} />
-              </Flex>
-              <Flex gap={2}>
-                <ModelAttrView label={t('modelManager.ztsnrTraining')} value={`${modelData.ztsnr_training}`} />
-                <ModelAttrView label={t('modelManager.vae')} value={modelData.vae} />
-              </Flex>
-            </>
+            <Flex gap={2}>
+              {modelData.format === 'diffusers' && modelData.repo_variant && (
+                <ModelAttrView label={t('modelManager.repoVariant')} value={modelData.repo_variant} />
+              )}
+              {modelData.format === 'checkpoint' && (
+                <>
+                  <ModelAttrView label={t('modelManager.pathToConfig')} value={modelData.config_path} />
+                  <ModelAttrView label={t('modelManager.variant')} value={modelData.variant} />
+                  <ModelAttrView label={t('modelManager.predictionType')} value={modelData.prediction_type} />
+                  <ModelAttrView label={t('modelManager.upcastAttention')} value={`${modelData.upcast_attention}`} />
+                </>
+              )}
+            </Flex>
           )}
           {modelData.type === 'ip_adapter' && (
             <Flex gap={2}>

invokeai/frontend/web/src/features/parameters/components/Core/ParamNegativePrompt.tsx

@@ -1,10 +1,10 @@
 import { Box, Textarea } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { AddEmbeddingButton } from 'features/embedding/AddEmbeddingButton';
-import { EmbeddingPopover } from 'features/embedding/EmbeddingPopover';
-import { usePrompt } from 'features/embedding/usePrompt';
 import { PromptOverlayButtonWrapper } from 'features/parameters/components/Prompts/PromptOverlayButtonWrapper';
 import { setNegativePrompt } from 'features/parameters/store/generationSlice';
+import { AddPromptTriggerButton } from 'features/prompt/AddPromptTriggerButton';
+import { PromptPopover } from 'features/prompt/PromptPopover';
+import { usePrompt } from 'features/prompt/usePrompt';
 import { memo, useCallback, useRef } from 'react';
 import { useTranslation } from 'react-i18next';
 
@@ -19,19 +19,14 @@ export const ParamNegativePrompt = memo(() => {
     },
     [dispatch]
   );
-  const { onChange, isOpen, onClose, onOpen, onSelectEmbedding, onKeyDown } = usePrompt({
+  const { onChange, isOpen, onClose, onOpen, onSelect, onKeyDown } = usePrompt({
     prompt,
     textareaRef,
     onChange: _onChange,
   });
 
   return (
-    <EmbeddingPopover
-      isOpen={isOpen}
-      onClose={onClose}
-      onSelect={onSelectEmbedding}
-      width={textareaRef.current?.clientWidth}
-    >
+    <PromptPopover isOpen={isOpen} onClose={onClose} onSelect={onSelect} width={textareaRef.current?.clientWidth}>
       <Box pos="relative">
         <Textarea
           id="negativePrompt"
@@ -45,10 +40,10 @@ export const ParamNegativePrompt = memo(() => {
           variant="darkFilled"
         />
         <PromptOverlayButtonWrapper>
-          <AddEmbeddingButton isOpen={isOpen} onOpen={onOpen} />
+          <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />
         </PromptOverlayButtonWrapper>
       </Box>
-    </EmbeddingPopover>
+    </PromptPopover>
   );
 });
 

invokeai/frontend/web/src/features/parameters/components/Core/ParamPositivePrompt.tsx

@@ -1,11 +1,11 @@
 import { Box, Textarea } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { ShowDynamicPromptsPreviewButton } from 'features/dynamicPrompts/components/ShowDynamicPromptsPreviewButton';
-import { AddEmbeddingButton } from 'features/embedding/AddEmbeddingButton';
-import { EmbeddingPopover } from 'features/embedding/EmbeddingPopover';
-import { usePrompt } from 'features/embedding/usePrompt';
 import { PromptOverlayButtonWrapper } from 'features/parameters/components/Prompts/PromptOverlayButtonWrapper';
 import { setPositivePrompt } from 'features/parameters/store/generationSlice';
+import { AddPromptTriggerButton } from 'features/prompt/AddPromptTriggerButton';
+import { PromptPopover } from 'features/prompt/PromptPopover';
+import { usePrompt } from 'features/prompt/usePrompt';
 import { SDXLConcatButton } from 'features/sdxl/components/SDXLPrompts/SDXLConcatButton';
 import { memo, useCallback, useRef } from 'react';
 import type { HotkeyCallback } from 'react-hotkeys-hook';
@@ -25,7 +25,7 @@ export const ParamPositivePrompt = memo(() => {
     },
     [dispatch]
   );
-  const { onChange, isOpen, onClose, onOpen, onSelectEmbedding, onKeyDown, onFocus } = usePrompt({
+  const { onChange, isOpen, onClose, onOpen, onSelect, onKeyDown, onFocus } = usePrompt({
     prompt,
     textareaRef: textareaRef,
     onChange: handleChange,
@@ -42,12 +42,7 @@ export const ParamPositivePrompt = memo(() => {
   useHotkeys('alt+a', focus, []);
 
   return (
-    <EmbeddingPopover
-      isOpen={isOpen}
-      onClose={onClose}
-      onSelect={onSelectEmbedding}
-      width={textareaRef.current?.clientWidth}
-    >
+    <PromptPopover isOpen={isOpen} onClose={onClose} onSelect={onSelect} width={textareaRef.current?.clientWidth}>
       <Box pos="relative">
         <Textarea
           id="prompt"
@@ -61,12 +56,12 @@ export const ParamPositivePrompt = memo(() => {
           variant="darkFilled"
         />
         <PromptOverlayButtonWrapper>
-          <AddEmbeddingButton isOpen={isOpen} onOpen={onOpen} />
+          <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />
           {baseModel === 'sdxl' && <SDXLConcatButton />}
           <ShowDynamicPromptsPreviewButton />
         </PromptOverlayButtonWrapper>
       </Box>
-    </EmbeddingPopover>
+    </PromptPopover>
   );
 });
 

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -16,7 +16,6 @@ import type {
   ParameterScheduler,
   ParameterVAEModel,
 } from 'features/parameters/types/parameterSchemas';
-import { zParameterModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import { configChanged } from 'features/system/store/configSlice';
 import { clamp } from 'lodash-es';
@@ -210,26 +209,6 @@ export const generationSlice = createSlice({
   },
   extraReducers: (builder) => {
     builder.addCase(configChanged, (state, action) => {
-      const defaultModel = action.payload.sd?.defaultModel;
-
-      if (defaultModel && !state.model) {
-        const [base_model, model_type, model_name] = defaultModel.split('/');
-
-        const result = zParameterModel.safeParse({
-          model_name,
-          base_model,
-          model_type,
-        });
-
-        if (result.success) {
-          state.model = result.data;
-
-          const optimalDimension = getOptimalDimension(result.data);
-
-          state.width = optimalDimension;
-          state.height = optimalDimension;
-        }
-      }
       if (action.payload.sd?.scheduler) {
         state.scheduler = action.payload.sd.scheduler;
       }

invokeai/frontend/web/src/features/prompt/AddPromptTriggerButton.tsx

@@ -8,15 +8,15 @@ type Props = {
   onOpen: () => void;
 };
 
-export const AddEmbeddingButton = memo((props: Props) => {
+export const AddPromptTriggerButton = memo((props: Props) => {
   const { onOpen, isOpen } = props;
   const { t } = useTranslation();
   return (
-    <Tooltip label={t('embedding.addEmbedding')}>
+    <Tooltip label={t('prompt.addPromptTrigger')}>
       <IconButton
         variant="promptOverlay"
         isDisabled={isOpen}
-        aria-label={t('embedding.addEmbedding')}
+        aria-label={t('prompt.addPromptTrigger')}
         icon={<PiCodeBold />}
         onClick={onOpen}
       />
@@ -24,4 +24,4 @@ export const AddEmbeddingButton = memo((props: Props) => {
   );
 });
 
-AddEmbeddingButton.displayName = 'AddEmbeddingButton';
+AddPromptTriggerButton.displayName = 'AddPromptTriggerButton';

invokeai/frontend/web/src/features/prompt/PromptPopover.tsx

@@ -1,9 +1,9 @@
 import { Popover, PopoverAnchor, PopoverBody, PopoverContent } from '@invoke-ai/ui-library';
-import { EmbeddingSelect } from 'features/embedding/EmbeddingSelect';
-import type { EmbeddingPopoverProps } from 'features/embedding/types';
+import { PromptTriggerSelect } from 'features/prompt/PromptTriggerSelect';
+import type { PromptPopoverProps } from 'features/prompt/types';
 import { memo } from 'react';
 
-export const EmbeddingPopover = memo((props: EmbeddingPopoverProps) => {
+export const PromptPopover = memo((props: PromptPopoverProps) => {
   const { onSelect, isOpen, onClose, width, children } = props;
 
   return (
@@ -14,7 +14,7 @@ export const EmbeddingPopover = memo((props: EmbeddingPopoverProps) => {
       openDelay={0}
       closeDelay={0}
       closeOnBlur={true}
-      returnFocusOnClose={true}
+      returnFocusOnClose={false}
       isLazy
     >
       <PopoverAnchor>{children}</PopoverAnchor>
@@ -27,11 +27,11 @@ export const EmbeddingPopover = memo((props: EmbeddingPopoverProps) => {
         borderStyle="solid"
       >
         <PopoverBody p={0} width={`calc(${width}px - 0.25rem)`}>
-          <EmbeddingSelect onClose={onClose} onSelect={onSelect} />
+          <PromptTriggerSelect onClose={onClose} onSelect={onSelect} />
         </PopoverBody>
       </PopoverContent>
     </Popover>
   );
 });
 
-EmbeddingPopover.displayName = 'EmbeddingPopover';
+PromptPopover.displayName = 'PromptPopover';

invokeai/frontend/web/src/features/prompt/PromptTriggerSelect.stories.tsx

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

invokeai/frontend/web/src/features/prompt/PromptTriggerSelect.tsx

@@ -0,0 +1,104 @@
+import type { ChakraProps, ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
+import { Combobox, FormControl } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import type { GroupBase } from 'chakra-react-select';
+import { selectLoraSlice } from 'features/lora/store/loraSlice';
+import type { PromptTriggerSelectProps } from 'features/prompt/types';
+import { t } from 'i18next';
+import { flatten, map } from 'lodash-es';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import {
+  loraModelsAdapterSelectors,
+  textualInversionModelsAdapterSelectors,
+  useGetLoRAModelsQuery,
+  useGetTextualInversionModelsQuery,
+} from 'services/api/endpoints/models';
+
+const noOptionsMessage = () => t('prompt.noMatchingTriggers');
+
+const selectLoRAs = createMemoizedSelector(selectLoraSlice, (loras) => loras.loras);
+
+export const PromptTriggerSelect = memo(({ onSelect, onClose }: PromptTriggerSelectProps) => {
+  const { t } = useTranslation();
+
+  const currentBaseModel = useAppSelector((s) => s.generation.model?.base);
+  const addedLoRAs = useAppSelector(selectLoRAs);
+  const { data: loraModels, isLoading: isLoadingLoRAs } = useGetLoRAModelsQuery();
+  const { data: tiModels, isLoading: isLoadingTIs } = useGetTextualInversionModelsQuery();
+
+  const _onChange = useCallback<ComboboxOnChange>(
+    (v) => {
+      if (!v) {
+        onSelect('');
+        return;
+      }
+
+      onSelect(v.value);
+    },
+    [onSelect]
+  );
+
+  const options = useMemo(() => {
+    const _options: GroupBase<ComboboxOption>[] = [];
+
+    if (tiModels) {
+      const embeddingOptions = textualInversionModelsAdapterSelectors
+        .selectAll(tiModels)
+        .filter((ti) => ti.base === currentBaseModel)
+        .map((model) => ({ label: model.name, value: `<${model.name}>` }));
+
+      if (embeddingOptions.length > 0) {
+        _options.push({
+          label: t('prompt.compatibleEmbeddings'),
+          options: embeddingOptions,
+        });
+      }
+    }
+
+    if (loraModels) {
+      const triggerPhraseOptions = loraModelsAdapterSelectors
+        .selectAll(loraModels)
+        .filter((lora) => map(addedLoRAs, (l) => l.model.key).includes(lora.key))
+        .map((lora) => {
+          if (lora.trigger_phrases) {
+            return lora.trigger_phrases.map((triggerPhrase) => ({ label: triggerPhrase, value: triggerPhrase }));
+          }
+          return [];
+        })
+        .flatMap((x) => x);
+
+      if (triggerPhraseOptions.length > 0) {
+        _options.push({
+          label: t('modelManager.triggerPhrases'),
+          options: flatten(triggerPhraseOptions),
+        });
+      }
+    }
+    return _options;
+  }, [tiModels, loraModels, t, currentBaseModel, addedLoRAs]);
+
+  return (
+    <FormControl>
+      <Combobox
+        placeholder={isLoadingLoRAs || isLoadingTIs ? t('common.loading') : t('prompt.addPromptTrigger')}
+        defaultMenuIsOpen
+        autoFocus
+        value={null}
+        options={options}
+        noOptionsMessage={noOptionsMessage}
+        onChange={_onChange}
+        onMenuClose={onClose}
+        data-testid="add-prompt-trigger"
+        sx={selectStyles}
+      />
+    </FormControl>
+  );
+});
+
+PromptTriggerSelect.displayName = 'PromptTriggerSelect';
+
+const selectStyles: ChakraProps['sx'] = {
+  w: 'full',
+};

invokeai/frontend/web/src/features/prompt/types.ts

@@ -1,12 +1,12 @@
 import type { PropsWithChildren } from 'react';
 
-export type EmbeddingSelectProps = {
+export type PromptTriggerSelectProps = {
   onSelect: (v: string) => void;
   onClose: () => void;
 };
 
-export type EmbeddingPopoverProps = PropsWithChildren &
-  EmbeddingSelectProps & {
+export type PromptPopoverProps = PropsWithChildren &
+  PromptTriggerSelectProps & {
     isOpen: boolean;
     width?: number | string;
   };

invokeai/frontend/web/src/features/prompt/usePrompt.ts

@@ -4,13 +4,13 @@ import type { ChangeEventHandler, KeyboardEventHandler, RefObject } from 'react'
 import { useCallback } from 'react';
 import { flushSync } from 'react-dom';
 
-type UseInsertEmbeddingArg = {
+type UseInsertTriggerArg = {
   prompt: string;
   textareaRef: RefObject<HTMLTextAreaElement>;
   onChange: (v: string) => void;
 };
 
-export const usePrompt = ({ prompt, textareaRef, onChange: _onChange }: UseInsertEmbeddingArg) => {
+export const usePrompt = ({ prompt, textareaRef, onChange: _onChange }: UseInsertTriggerArg) => {
   const { isOpen, onClose, onOpen } = useDisclosure();
 
   const onChange: ChangeEventHandler<HTMLTextAreaElement> = useCallback(
@@ -20,13 +20,13 @@ export const usePrompt = ({ prompt, textareaRef, onChange: _onChange }: UseInser
     [_onChange]
   );
 
-  const insertEmbedding = useCallback(
+  const insertTrigger = useCallback(
     (v: string) => {
       if (!textareaRef.current) {
         return;
       }
 
-      // this is where we insert the TI trigger
+      // this is where we insert the trigger
       const caret = textareaRef.current.selectionStart;
 
       if (isNil(caret)) {
@@ -35,13 +35,9 @@ export const usePrompt = ({ prompt, textareaRef, onChange: _onChange }: UseInser
 
       let newPrompt = prompt.slice(0, caret);
 
-      if (newPrompt[newPrompt.length - 1] !== '<') {
-        newPrompt += '<';
-      }
+      newPrompt += `${v}`;
 
-      newPrompt += `${v}>`;
-
-      // we insert the cursor after the `>`
+      // we insert the cursor after the end of trigger
       const finalCaretPos = newPrompt.length;
 
       newPrompt += prompt.slice(caret);
@@ -51,7 +47,7 @@ export const usePrompt = ({ prompt, textareaRef, onChange: _onChange }: UseInser
         _onChange(newPrompt);
       });
 
-      // set the caret position to just after the TI trigger
+      // set the cursor position to just after the trigger
       textareaRef.current.selectionStart = finalCaretPos;
       textareaRef.current.selectionEnd = finalCaretPos;
     },
@@ -62,17 +58,17 @@ export const usePrompt = ({ prompt, textareaRef, onChange: _onChange }: UseInser
     textareaRef.current?.focus();
   }, [textareaRef]);
 
-  const handleClose = useCallback(() => {
+  const handleClosePopover = useCallback(() => {
     onClose();
     onFocus();
   }, [onFocus, onClose]);
 
-  const onSelectEmbedding = useCallback(
+  const onSelect = useCallback(
     (v: string) => {
-      insertEmbedding(v);
-      handleClose();
+      insertTrigger(v);
+      handleClosePopover();
     },
-    [handleClose, insertEmbedding]
+    [handleClosePopover, insertTrigger]
   );
 
   const onKeyDown: KeyboardEventHandler<HTMLTextAreaElement> = useCallback(
@@ -90,7 +86,7 @@ export const usePrompt = ({ prompt, textareaRef, onChange: _onChange }: UseInser
     isOpen,
     onClose,
     onOpen,
-    onSelectEmbedding,
+    onSelect,
     onKeyDown,
     onFocus,
   };

invokeai/frontend/web/src/features/sdxl/components/SDXLPrompts/ParamSDXLNegativeStylePrompt.tsx

@@ -1,9 +1,9 @@
 import { Box, Textarea } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { AddEmbeddingButton } from 'features/embedding/AddEmbeddingButton';
-import { EmbeddingPopover } from 'features/embedding/EmbeddingPopover';
-import { usePrompt } from 'features/embedding/usePrompt';
 import { PromptOverlayButtonWrapper } from 'features/parameters/components/Prompts/PromptOverlayButtonWrapper';
+import { AddPromptTriggerButton } from 'features/prompt/AddPromptTriggerButton';
+import { PromptPopover } from 'features/prompt/PromptPopover';
+import { usePrompt } from 'features/prompt/usePrompt';
 import { setNegativeStylePromptSDXL } from 'features/sdxl/store/sdxlSlice';
 import { memo, useCallback, useRef } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
@@ -20,7 +20,7 @@ export const ParamSDXLNegativeStylePrompt = memo(() => {
     },
     [dispatch]
   );
-  const { onChange, isOpen, onClose, onOpen, onSelectEmbedding, onKeyDown, onFocus } = usePrompt({
+  const { onChange, isOpen, onClose, onOpen, onSelect, onKeyDown, onFocus } = usePrompt({
     prompt,
     textareaRef: textareaRef,
     onChange: handleChange,
@@ -29,12 +29,7 @@ export const ParamSDXLNegativeStylePrompt = memo(() => {
   useHotkeys('alt+a', onFocus, []);
 
   return (
-    <EmbeddingPopover
-      isOpen={isOpen}
-      onClose={onClose}
-      onSelect={onSelectEmbedding}
-      width={textareaRef.current?.clientWidth}
-    >
+    <PromptPopover isOpen={isOpen} onClose={onClose} onSelect={onSelect} width={textareaRef.current?.clientWidth}>
       <Box pos="relative">
         <Textarea
           id="prompt"
@@ -48,10 +43,10 @@ export const ParamSDXLNegativeStylePrompt = memo(() => {
           variant="darkFilled"
         />
         <PromptOverlayButtonWrapper>
-          <AddEmbeddingButton isOpen={isOpen} onOpen={onOpen} />
+          <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />
         </PromptOverlayButtonWrapper>
       </Box>
-    </EmbeddingPopover>
+    </PromptPopover>
   );
 });
 

invokeai/frontend/web/src/features/sdxl/components/SDXLPrompts/ParamSDXLPositiveStylePrompt.tsx

@@ -1,9 +1,9 @@
 import { Box, Textarea } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { AddEmbeddingButton } from 'features/embedding/AddEmbeddingButton';
-import { EmbeddingPopover } from 'features/embedding/EmbeddingPopover';
-import { usePrompt } from 'features/embedding/usePrompt';
 import { PromptOverlayButtonWrapper } from 'features/parameters/components/Prompts/PromptOverlayButtonWrapper';
+import { AddPromptTriggerButton } from 'features/prompt/AddPromptTriggerButton';
+import { PromptPopover } from 'features/prompt/PromptPopover';
+import { usePrompt } from 'features/prompt/usePrompt';
 import { setPositiveStylePromptSDXL } from 'features/sdxl/store/sdxlSlice';
 import { memo, useCallback, useRef } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -19,19 +19,14 @@ export const ParamSDXLPositiveStylePrompt = memo(() => {
     },
     [dispatch]
   );
-  const { onChange, isOpen, onClose, onOpen, onSelectEmbedding, onKeyDown } = usePrompt({
+  const { onChange, isOpen, onClose, onOpen, onSelect, onKeyDown } = usePrompt({
     prompt,
     textareaRef: textareaRef,
     onChange: handleChange,
   });
 
   return (
-    <EmbeddingPopover
-      isOpen={isOpen}
-      onClose={onClose}
-      onSelect={onSelectEmbedding}
-      width={textareaRef.current?.clientWidth}
-    >
+    <PromptPopover isOpen={isOpen} onClose={onClose} onSelect={onSelect} width={textareaRef.current?.clientWidth}>
       <Box pos="relative">
         <Textarea
           id="prompt"
@@ -45,10 +40,10 @@ export const ParamSDXLPositiveStylePrompt = memo(() => {
           variant="darkFilled"
         />
         <PromptOverlayButtonWrapper>
-          <AddEmbeddingButton isOpen={isOpen} onOpen={onOpen} />
+          <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />
         </PromptOverlayButtonWrapper>
       </Box>
-    </EmbeddingPopover>
+    </PromptPopover>
   );
 });
 

invokeai/frontend/web/src/services/api/endpoints/models.ts

@@ -1,7 +1,6 @@
 import type { EntityAdapter, EntityState, ThunkDispatch, UnknownAction } from '@reduxjs/toolkit';
 import { createEntityAdapter } from '@reduxjs/toolkit';
 import { getSelectorsOptions } from 'app/store/createMemoizedSelector';
-import type { JSONObject } from 'common/types';
 import queryString from 'query-string';
 import type { operations, paths } from 'services/api/schema';
 import type {
@@ -24,49 +23,33 @@ export type UpdateModelArg = {
   body: paths['/api/v2/models/i/{key}']['patch']['requestBody']['content']['application/json'];
 };
 
-type UpdateModelMetadataArg = {
-  key: paths['/api/v2/models/i/{key}/metadata']['patch']['parameters']['path']['key'];
-  body: paths['/api/v2/models/i/{key}/metadata']['patch']['requestBody']['content']['application/json'];
-};
-
 type UpdateModelResponse = paths['/api/v2/models/i/{key}']['patch']['responses']['200']['content']['application/json'];
-type UpdateModelMetadataResponse =
-  paths['/api/v2/models/i/{key}/metadata']['patch']['responses']['200']['content']['application/json'];
 
 type GetModelConfigResponse = paths['/api/v2/models/i/{key}']['get']['responses']['200']['content']['application/json'];
 
-type GetModelMetadataResponse =
-  paths['/api/v2/models/i/{key}/metadata']['get']['responses']['200']['content']['application/json'];
-
 type ListModelsArg = NonNullable<paths['/api/v2/models/']['get']['parameters']['query']>;
 
-type DeleteMainModelArg = {
+type DeleteModelArg = {
   key: string;
 };
-
-type DeleteMainModelResponse = void;
+type DeleteModelResponse = void;
 
 type ConvertMainModelResponse =
   paths['/api/v2/models/convert/{key}']['put']['responses']['200']['content']['application/json'];
 
 type InstallModelArg = {
   source: paths['/api/v2/models/install']['post']['parameters']['query']['source'];
-  access_token?: paths['/api/v2/models/install']['post']['parameters']['query']['access_token'];
-  // TODO(MM2): This is typed as `Optional[Dict[str, Any]]` in backend...
-  config?: JSONObject;
-  // config: NonNullable<paths['/api/v2/models/install']['post']['requestBody']>['content']['application/json'];
 };
-
 type InstallModelResponse = paths['/api/v2/models/install']['post']['responses']['201']['content']['application/json'];
 
-type ListImportModelsResponse =
-  paths['/api/v2/models/import']['get']['responses']['200']['content']['application/json'];
+type ListModelInstallsResponse =
+  paths['/api/v2/models/install']['get']['responses']['200']['content']['application/json'];
 
-type DeleteImportModelsResponse =
-  paths['/api/v2/models/import/{id}']['delete']['responses']['201']['content']['application/json'];
+type CancelModelInstallResponse =
+  paths['/api/v2/models/install/{id}']['delete']['responses']['201']['content']['application/json'];
 
-type PruneModelImportsResponse =
-  paths['/api/v2/models/import']['patch']['responses']['200']['content']['application/json'];
+type PruneCompletedModelInstallsResponse =
+  paths['/api/v2/models/install']['delete']['responses']['200']['content']['application/json'];
 
 export type ScanFolderResponse =
   paths['/api/v2/models/scan_folder']['get']['responses']['200']['content']['application/json'];
@@ -83,6 +66,7 @@ const loraModelsAdapter = createEntityAdapter<LoRAModelConfig, string>({
   selectId: (entity) => entity.key,
   sortComparer: (a, b) => a.name.localeCompare(b.name),
 });
+export const loraModelsAdapterSelectors = loraModelsAdapter.getSelectors(undefined, getSelectorsOptions);
 const controlNetModelsAdapter = createEntityAdapter<ControlNetModelConfig, string>({
   selectId: (entity) => entity.key,
   sortComparer: (a, b) => a.name.localeCompare(b.name),
@@ -102,6 +86,10 @@ const textualInversionModelsAdapter = createEntityAdapter<TextualInversionModelC
   selectId: (entity) => entity.key,
   sortComparer: (a, b) => a.name.localeCompare(b.name),
 });
+export const textualInversionModelsAdapterSelectors = textualInversionModelsAdapter.getSelectors(
+  undefined,
+  getSelectorsOptions
+);
 const vaeModelsAdapter = createEntityAdapter<VAEModelConfig, string>({
   selectId: (entity) => entity.key,
   sortComparer: (a, b) => a.name.localeCompare(b.name),
@@ -146,31 +134,7 @@ const buildModelsUrl = (path: string = '') => buildV2Url(`models/${path}`);
 
 export const modelsApi = api.injectEndpoints({
   endpoints: (build) => ({
-    getMainModels: build.query<EntityState<MainModelConfig, string>, BaseModelType[]>({
-      query: (base_models) => {
-        const params: ListModelsArg = {
-          model_type: 'main',
-          base_models,
-        };
-
-        const query = queryString.stringify(params, { arrayFormat: 'none' });
-        return buildModelsUrl(`?${query}`);
-      },
-      providesTags: buildProvidesTags<MainModelConfig>('MainModel'),
-      transformResponse: buildTransformResponse<MainModelConfig>(mainModelsAdapter),
-      onQueryStarted: async (_, { dispatch, queryFulfilled }) => {
-        queryFulfilled.then(({ data }) => {
-          upsertModelConfigs(data, dispatch);
-        });
-      },
-    }),
-    getModelMetadata: build.query<GetModelMetadataResponse, string>({
-      query: (key) => {
-        return buildModelsUrl(`i/${key}/metadata`);
-      },
-      providesTags: ['Model'],
-    }),
-    updateModels: build.mutation<UpdateModelResponse, UpdateModelArg>({
+    updateModel: build.mutation<UpdateModelResponse, UpdateModelArg>({
       query: ({ key, body }) => {
         return {
           url: buildModelsUrl(`i/${key}`),
@@ -180,28 +144,17 @@ export const modelsApi = api.injectEndpoints({
       },
       invalidatesTags: ['Model'],
     }),
-    updateModelMetadata: build.mutation<UpdateModelMetadataResponse, UpdateModelMetadataArg>({
-      query: ({ key, body }) => {
-        return {
-          url: buildModelsUrl(`i/${key}/metadata`),
-          method: 'PATCH',
-          body: body,
-        };
-      },
-      invalidatesTags: ['Model'],
-    }),
     installModel: build.mutation<InstallModelResponse, InstallModelArg>({
-      query: ({ source, config, access_token }) => {
+      query: ({ source }) => {
         return {
           url: buildModelsUrl('install'),
-          params: { source, access_token },
+          params: { source },
           method: 'POST',
-          body: config,
         };
       },
-      invalidatesTags: ['Model', 'ModelImports'],
+      invalidatesTags: ['Model', 'ModelInstalls'],
     }),
-    deleteModels: build.mutation<DeleteMainModelResponse, DeleteMainModelArg>({
+    deleteModels: build.mutation<DeleteModelResponse, DeleteModelArg>({
       query: ({ key }) => {
         return {
           url: buildModelsUrl(`i/${key}`),
@@ -210,7 +163,7 @@ export const modelsApi = api.injectEndpoints({
       },
       invalidatesTags: ['Model'],
     }),
-    convertMainModels: build.mutation<ConvertMainModelResponse, string>({
+    convertModel: build.mutation<ConvertMainModelResponse, string>({
       query: (key) => {
         return {
           url: buildModelsUrl(`convert/${key}`),
@@ -253,6 +206,57 @@ export const modelsApi = api.injectEndpoints({
       },
       invalidatesTags: ['Model'],
     }),
+    scanFolder: build.query<ScanFolderResponse, ScanFolderArg>({
+      query: (arg) => {
+        const folderQueryStr = arg ? queryString.stringify(arg, {}) : '';
+        return {
+          url: buildModelsUrl(`scan_folder?${folderQueryStr}`),
+        };
+      },
+    }),
+    listModelInstalls: build.query<ListModelInstallsResponse, void>({
+      query: () => {
+        return {
+          url: buildModelsUrl('install'),
+        };
+      },
+      providesTags: ['ModelInstalls'],
+    }),
+    cancelModelInstall: build.mutation<CancelModelInstallResponse, number>({
+      query: (id) => {
+        return {
+          url: buildModelsUrl(`install/${id}`),
+          method: 'DELETE',
+        };
+      },
+      invalidatesTags: ['ModelInstalls'],
+    }),
+    pruneCompletedModelInstalls: build.mutation<PruneCompletedModelInstallsResponse, void>({
+      query: () => {
+        return {
+          url: buildModelsUrl('install'),
+          method: 'DELETE',
+        };
+      },
+      invalidatesTags: ['ModelInstalls'],
+    }),
+    getMainModels: build.query<EntityState<MainModelConfig, string>, BaseModelType[]>({
+      query: (base_models) => {
+        const params: ListModelsArg = {
+          model_type: 'main',
+          base_models,
+        };
+        const query = queryString.stringify(params, { arrayFormat: 'none' });
+        return buildModelsUrl(`?${query}`);
+      },
+      providesTags: buildProvidesTags<MainModelConfig>('MainModel'),
+      transformResponse: buildTransformResponse<MainModelConfig>(mainModelsAdapter),
+      onQueryStarted: async (_, { dispatch, queryFulfilled }) => {
+        queryFulfilled.then(({ data }) => {
+          upsertModelConfigs(data, dispatch);
+        });
+      },
+    }),
     getLoRAModels: build.query<EntityState<LoRAModelConfig, string>, void>({
       query: () => ({ url: buildModelsUrl(), params: { model_type: 'lora' } }),
       providesTags: buildProvidesTags<LoRAModelConfig>('LoRAModel'),
@@ -313,40 +317,6 @@ export const modelsApi = api.injectEndpoints({
         });
       },
     }),
-    scanModels: build.query<ScanFolderResponse, ScanFolderArg>({
-      query: (arg) => {
-        const folderQueryStr = arg ? queryString.stringify(arg, {}) : '';
-        return {
-          url: buildModelsUrl(`scan_folder?${folderQueryStr}`),
-        };
-      },
-    }),
-    getModelImports: build.query<ListImportModelsResponse, void>({
-      query: () => {
-        return {
-          url: buildModelsUrl(`import`),
-        };
-      },
-      providesTags: ['ModelImports'],
-    }),
-    deleteModelImport: build.mutation<DeleteImportModelsResponse, number>({
-      query: (id) => {
-        return {
-          url: buildModelsUrl(`import/${id}`),
-          method: 'DELETE',
-        };
-      },
-      invalidatesTags: ['ModelImports'],
-    }),
-    pruneModelImports: build.mutation<PruneModelImportsResponse, void>({
-      query: () => {
-        return {
-          url: buildModelsUrl('import'),
-          method: 'PATCH',
-        };
-      },
-      invalidatesTags: ['ModelImports'],
-    }),
   }),
 });
 
@@ -360,16 +330,14 @@ export const {
   useGetTextualInversionModelsQuery,
   useGetVaeModelsQuery,
   useDeleteModelsMutation,
-  useUpdateModelsMutation,
+  useUpdateModelMutation,
   useInstallModelMutation,
-  useConvertMainModelsMutation,
+  useConvertModelMutation,
   useSyncModelsMutation,
-  useLazyScanModelsQuery,
-  useGetModelImportsQuery,
-  useGetModelMetadataQuery,
-  useDeleteModelImportMutation,
-  usePruneModelImportsMutation,
-  useUpdateModelMetadataMutation,
+  useLazyScanFolderQuery,
+  useListModelInstallsQuery,
+  useCancelModelInstallMutation,
+  usePruneCompletedModelInstallsMutation,
 } = modelsApi;
 
 const upsertModelConfigs = (

invokeai/frontend/web/src/services/api/index.ts

@@ -28,7 +28,7 @@ export const tagTypes = [
   'InvocationCacheStatus',
   'Model',
   'ModelConfig',
-  'ModelImports',
+  'ModelInstalls',
   'T2IAdapterModel',
   'MainModel',
   'VaeModel',

invokeai/frontend/web/src/services/api/types.ts

@@ -43,14 +43,13 @@ export type ControlField = S['ControlField'];
 // Model Configs
 
 // TODO(MM2): Can we make key required in the pydantic model?
-export type LoRAModelConfig = S['LoRAConfig'];
+export type LoRAModelConfig = S['LoRADiffusersConfig'] | S['LoRALyCORISConfig'];
 // TODO(MM2): Can we rename this from Vae -> VAE
-export type VAEModelConfig = S['VaeCheckpointConfig'] | S['VaeDiffusersConfig'];
+export type VAEModelConfig = S['VAECheckpointConfig'] | S['VAEDiffusersConfig'];
 export type ControlNetModelConfig = S['ControlNetDiffusersConfig'] | S['ControlNetCheckpointConfig'];
 export type IPAdapterModelConfig = S['IPAdapterConfig'];
-// TODO(MM2): Can we rename this to T2IAdapterConfig
-export type T2IAdapterModelConfig = S['T2IConfig'];
-export type TextualInversionModelConfig = S['TextualInversionConfig'];
+export type T2IAdapterModelConfig = S['T2IAdapterConfig'];
+export type TextualInversionModelConfig = S['TextualInversionFileConfig'] | S['TextualInversionFolderConfig'];
 export type DiffusersModelConfig = S['MainDiffusersConfig'];
 export type CheckpointModelConfig = S['MainCheckpointConfig'];
 type CLIPVisionDiffusersConfig = S['CLIPVisionDiffusersConfig'];