[DO NOT MERGE] Label Testing PR

Added ideal size node

.github/pr_labels.yml

@@ -0,0 +1,40 @@
+Root:
+- changed-files:
+  - any-glob-to-any-file: '*'
+
+PythonDeps:
+- changed-files:
+  - any-glob-to-any-file: 'pyproject.toml'
+
+Python:
+- changed-files:
+  - any-glob-to-any-file: 
+    - 'invokeai/**'
+    - '!invokeai/frontend/web/**'
+    - 'tests/**'
+
+Invocations:
+- changed-files:
+  - any-glob-to-any-file: 'invokeai/app/invocations/**'
+
+Backend:
+- changed-files:
+  - any-glob-to-any-file: 'invokeai/backend/**'
+
+Api:
+- changed-files:
+  - any-glob-to-any-file: 'invokeai/app/api/**'
+
+Services:
+- changed-files:
+  - any-glob-to-any-file: 'invokeai/app/services/**'
+
+FrontendDeps:
+- changed-files:
+  - any-glob-to-any-file:
+    - '**/*/package.json'
+    - '**/*/pnpm-lock.yaml'
+
+Frontend:
+- changed-files:
+  - any-glob-to-any-file: 'invokeai/frontend/web/**'

.github/workflows/label-pr.yml

@@ -0,0 +1,16 @@
+name: "Pull Request Labeler"
+on:
+- pull_request_target
+
+jobs:
+  labeler:
+    permissions:
+      contents: read
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - uses: actions/labeler@v5
+        with:
+          configuration-path: .github/pr_labels.yml

.github/workflows/test-invoke-pip.yml

@@ -58,7 +58,7 @@ jobs:
 
       - name: Check for changed python files
         id: changed-files
-        uses: tj-actions/changed-files@v37
+        uses: tj-actions/changed-files@v41
         with:
           files_yaml: |
             python:

docs/contributing/MODEL_MANAGER.md

@@ -15,8 +15,13 @@ model. These are the:
   their metadata, and `ModelRecordServiceBase` to store that
   information. It is also responsible for managing the InvokeAI
   `models` directory and its contents.
-
-* _DownloadQueueServiceBase_ (**CURRENTLY UNDER DEVELOPMENT - NOT IMPLEMENTED**)
+  
+* _ModelMetadataStore_ and _ModelMetaDataFetch_ Backend modules that
+  are able to retrieve metadata from online model repositories,
+  transform them into Pydantic models, and cache them to the InvokeAI
+  SQL database.
+  
+* _DownloadQueueServiceBase_
   A multithreaded downloader responsible
   for downloading models from a remote source to disk. The download
   queue has special methods for downloading repo_id folders from
@@ -30,13 +35,13 @@ model. These are the:
   
 ## Location of the Code
 
-All four of these services can be found in
+The four main services can be found in
 `invokeai/app/services` in the following directories:
 
 * `invokeai/app/services/model_records/`
 * `invokeai/app/services/model_install/`
+* `invokeai/app/services/downloads/`
 * `invokeai/app/services/model_loader/` (**under development**)
-* `invokeai/app/services/downloads/`(**under development**)
 
 Code related to the FastAPI web API can be found in
 `invokeai/app/api/routers/model_records.py`.
@@ -402,15 +407,18 @@ functionality:
   the download, installation and registration process.
   
 - Downloading a model from an arbitrary URL and installing it in
-  `models_dir` (_implementation pending_).
+  `models_dir`.
   
 - Special handling for Civitai model URLs which allow the user to
-  paste in a model page's URL or download link (_implementation pending_).
-
+  paste in a model page's URL or download link
   
 - Special handling for HuggingFace repo_ids to recursively download
   the contents of the repository, paying attention to alternative
-  variants such as fp16. (_implementation pending_)
+  variants such as fp16.
+  
+- 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).
   
 ### Initializing the installer
 
@@ -426,16 +434,24 @@ following initialization pattern:
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.model_records import ModelRecordServiceSQL
 from invokeai.app.services.model_install import ModelInstallService
+from invokeai.app.services.download import DownloadQueueService
 from invokeai.app.services.shared.sqlite import SqliteDatabase
 from invokeai.backend.util.logging import InvokeAILogger
 
 config = InvokeAIAppConfig.get_config()
 config.parse_args()
+
 logger = InvokeAILogger.get_logger(config=config)
 db = SqliteDatabase(config, logger)
+record_store = ModelRecordServiceSQL(db)
+queue = DownloadQueueService()
+queue.start()
 
-store = ModelRecordServiceSQL(db)
-installer = ModelInstallService(config, store)
+installer = ModelInstallService(app_config=config, 
+                                record_store=record_store,
+						        download_queue=queue
+							    )
+installer.start()
 ```
 
 The full form of `ModelInstallService()` takes the following
@@ -443,9 +459,12 @@ required parameters:
 
 | **Argument**     | **Type**                     | **Description**              |
 |------------------|------------------------------|------------------------------|
-| `config`         | InvokeAIAppConfig       | InvokeAI app configuration object |
+| `app_config`         | InvokeAIAppConfig       | InvokeAI app configuration object |
 | `record_store`   | ModelRecordServiceBase  | Config record storage database |
-| `event_bus`      | EventServiceBase        | Optional event bus to send download/install progress events to |
+| `download_queue`   | DownloadQueueServiceBase  | Download queue object |
+| `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:
 
@@ -474,14 +493,14 @@ source7 = URLModelSource(url='https://civitai.com/api/download/models/63006', ac
 for source in [source1, source2, source3, source4, source5, source6, source7]:
    install_job = installer.install_model(source)
    
-source2job = installer.wait_for_installs()
+source2job = installer.wait_for_installs(timeout=120)
 for source in sources:
     job = source2job[source]
-	if job.status == "completed":
+	if job.complete:
 		model_config = job.config_out
 		model_key = model_config.key
 		print(f"{source} installed as {model_key}")
-	elif job.status == "error":
+	elif job.errored:
 	    print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
 	
 ```
@@ -515,43 +534,117 @@ The full list of arguments to `import_model()` is as follows:
 
 | **Argument**     | **Type**                     | **Default** | **Description**                           |
 |------------------|------------------------------|-------------|-------------------------------------------|
-| `source`         | Union[str, Path, AnyHttpUrl] |             | The source of the model, Path, URL or repo_id |
-| `inplace`        | bool                         | True        | Leave a local model in its current location |
-| `variant`        | str                          | None        | Desired variant, such as 'fp16' or 'onnx' (HuggingFace only) |
-| `subfolder`      | str                          | None        | Repository subfolder (HuggingFace only)   |
+| `source`         | ModelSource                 | None        | The source of the model, Path, URL or repo_id |
 | `config`         | Dict[str, Any]               | None        | Override all or a portion of model's probed attributes |
-| `access_token`   | str                          | None        | Provide authorization information needed to download |
 
-
-The `inplace` field controls how local model Paths are handled. If
-True (the default), then the model is simply registered in its current
-location by the installer's `ModelConfigRecordService`. Otherwise, a
-copy of the model put into the location specified by the `models_dir`
-application configuration parameter.
-
-The `variant` field is used for HuggingFace repo_ids only. If
-provided, the repo_id download handler will look for and download
-tensors files that follow the convention for the selected variant:
-
-- "fp16" will select files named "*model.fp16.{safetensors,bin}"
-- "onnx" will select files ending with the suffix ".onnx"
-- "openvino" will select files beginning with "openvino_model"
-
-In the special case of the "fp16" variant, the installer will select
-the 32-bit version of the files if the 16-bit version is unavailable.
-
-`subfolder` is used for HuggingFace repo_ids only. If provided, the
-model will be downloaded from the designated subfolder rather than the
-top-level repository folder. If a subfolder is attached to the repo_id
-using the format `repo_owner/repo_name:subfolder`, then the subfolder
-specified by the repo_id will override the subfolder argument.
+The next few sections describe the various types of ModelSource that
+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
 details.
 
-`access_token` is passed to the download queue and used to access
-repositories that require it.
+
+#### LocalModelSource
+
+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 |
+| `inplace`        | bool                         | False       | If set, the model file(s) will be left in their location; otherwise they will be copied into the InvokeAI root's `models` directory |
+
+#### URLModelSource
+
+This is used for a single-file model that is accessible via a URL. The
+fields are:
+
+| **Argument**     | **Type**                     | **Default** | **Description**                           |
+|------------------|------------------------------|-------------|-------------------------------------------|
+| `url`            | AnyHttpUrl                   | None        | The URL for the model file. |
+| `access_token`   | str                          | None        | An access token needed to gain access to this file. |
+
+The `AnyHttpUrl` class can be imported from `pydantic.networks`.
+
+Ordinarily, no metadata is retrieved from these sources. However,
+there is special-case code in the installer that looks for HuggingFace
+and Civitai URLs and fetches the corresponding model metadata from
+the corresponding repo.
+
+#### CivitaiModelSource
+
+This is used for a model that is hosted by the Civitai web site.
+
+| **Argument**     | **Type**                     | **Default** | **Description**                           |
+|------------------|------------------------------|-------------|-------------------------------------------|
+| `version_id`     | int                          | None        | The ID of the particular version of the desired model. |
+| `access_token`   | str                          | None        | An access token needed to gain access to a subscriber's-only model. |
+
+Civitai has two model IDs, both of which are integers. The `model_id`
+corresponds to a collection of model versions that may different in
+arbitrary ways, such as derivation from different checkpoint training
+steps, SFW vs NSFW generation, pruned vs non-pruned, etc. The
+`version_id` points to a specific version. Please use the latter.
+
+Some Civitai models require an access token to download. These can be
+generated from the Civitai profile page of a logged-in
+account. Somewhat annoyingly, if you fail to provide the access token
+when downloading a model that needs it, Civitai generates a redirect
+to a login page rather than a 403 Forbidden error. The installer
+attempts to catch this event and issue an informative error
+message. Otherwise you will get an "unrecognized model suffix" error
+when the model prober tries to identify the type of the HTML login
+page.
+
+#### HFModelSource
+
+HuggingFace has the most complicated `ModelSource` structure:
+
+| **Argument**     | **Type**                     | **Default** | **Description**                           |
+|------------------|------------------------------|-------------|-------------------------------------------|
+| `repo_id`        | str                          | None        | The ID of the desired model. |
+| `variant`        | ModelRepoVariant             | ModelRepoVariant('fp16')      | The desired variant. |
+| `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
+supports and is used to pick out from the hodgepodge of files that in
+a typical HuggingFace repository the particular components needed for
+a complete diffusers model. `ModelRepoVariant` is an enum that can be
+imported from `invokeai.backend.model_manager` and has the following
+values:
+
+| **Name**                   | **String Value**          |
+|----------------------------|---------------------------|
+| ModelRepoVariant.DEFAULT   | "default"                 |
+| ModelRepoVariant.FP16      | "fp16"                 |
+| ModelRepoVariant.FP32      | "fp32"                 |
+| ModelRepoVariant.ONNX      | "onnx"                 |
+| ModelRepoVariant.OPENVINO  | "openvino"             |
+| ModelRepoVariant.FLAX      | "flax"                 |
+
+You can also pass the string forms to `variant` directly. Note that
+InvokeAI may not be able to load and run all variants. At the current
+time, specifying `ModelRepoVariant.DEFAULT` will retrieve model files
+that are unqualified, e.g. `pytorch_model.safetensors` rather than
+`pytorch_model.fp16.safetensors`. These are usually the 32-bit
+safetensors forms of the model.
+
+If `subfolder` is specified, then the requested model resides in a
+subfolder of the main model repository. This is typically used to
+fetch and install VAEs.
+
+Some models require you to be registered with HuggingFace and logged
+in. To download these files, you must provide an
+`access_token`. Internally, if no access token is provided, then
+`HfFolder.get_token()` will be called to fill it in with the cached
+one.
+
 
 #### Monitoring the install job process
 
@@ -563,7 +656,8 @@ The `ModelInstallJob` class has the following structure:
 
 | **Attribute** | **Type**        |  **Description** |
 |----------------|-----------------|------------------|
-| `status`       | `InstallStatus`  | An enum of ["waiting", "running", "completed" and "error" |
+| `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 | 
@@ -578,30 +672,70 @@ broadcast to the InvokeAI event bus. The events will appear on the bus
 as an event of type `EventServiceBase.model_event`, a timestamp and
 the following event names:
 
-- `model_install_started`
+##### `model_install_downloading`
 
-The payload will contain the keys `timestamp` and `source`. The latter
-indicates the requested model source for installation.
+For remote models only, `model_install_downloading` events will be issued at regular
+intervals as the download progresses. The event's payload contains the
+following keys:
 
-- `model_install_progress`
+| **Key** | **Type**        |  **Description** |
+|----------------|-----------|------------------|
+| `source`       | str       | String representation of the requested source |
+| `local_path`   | str       | String representation of the path to the downloading model (usually a temporary directory) |
+| `bytes`        | int       | How many bytes downloaded so far |
+| `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 |
 
-Emitted at regular intervals when downloading a remote model, the
-payload will contain the keys `timestamp`, `source`, `current_bytes`
-and `total_bytes`. These events are _not_ emitted when a local model
-already on the filesystem is imported.
 
-- `model_install_completed`
+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.
 
-Issued once at the end of a successful installation. The payload will
-contain the keys `timestamp`, `source` and `key`, where `key` is the
-ID under which the model has been registered.
+Note that downloading events will not be issued for local models, and
+that downloading events occur *before* the running event.
 
-- `model_install_error`
+##### `model_install_running`
+
+`model_install_running` is issued when all the required downloads have completed (if applicable) and the
+model probing, copying and registration process has now started.
+
+The payload will contain the key `source`.
+
+##### `model_install_completed`
+
+`model_install_completed` is issued once at the end of a successful
+installation. The payload will contain the keys `source`,
+`total_bytes` and `key`, where `key` is the ID under which the model
+has been registered.
+
+##### `model_install_error`
+
+`model_install_error` is emitted if the installation process fails for
+some reason. The payload will contain the keys `source`, `error_type`
+and `error`. `error_type` is a short message indicating the nature of
+the error, and `error` is the long traceback to help debug the
+problem.
+
+##### `model_install_cancelled`
+
+`model_install_cancelled` is issued if the model installation is
+cancelled, or if one or more of its files' downloads are
+cancelled. The payload will contain `source`.
+
+##### Following the model status
+
+You may poll the `ModelInstallJob` object returned by `import_model()`
+to ascertain the state of the install. The job status can be read from
+the job's `status` attribute, an `InstallStatus` enum which has the
+enumerated values `WAITING`, `DOWNLOADING`, `RUNNING`, `COMPLETED`,
+`ERROR` and `CANCELLED`.
+
+For convenience, install jobs also provided the following boolean
+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.
 
-Emitted if the installation process fails for some reason. The payload
-will contain the keys `timestamp`, `source`, `error_type` and
-`error`. `error_type` is a short message indicating the nature of the
-error, and `error` is the long traceback to help debug the problem.
 
 #### Model confguration and probing
 
@@ -621,17 +755,9 @@ overriding values for any of the model's configuration
 attributes. Here is an example of setting the
 `SchedulerPredictionType` and `name` for an sd-2 model:
 
-This is typically used to set
-the model's name and description, but can also be used to overcome
-cases in which automatic probing is unable to (correctly) determine
-the model's attribute. The most common situation is the
-`prediction_type` field for sd-2 (and rare sd-1) models. Here is an
-example of how it works:
-
 ```
 install_job = installer.import_model(
-               source='stabilityai/stable-diffusion-2-1',
-			   variant='fp16',
+               source=HFModelSource(repo_id='stabilityai/stable-diffusion-2-1',variant='fp32'),
 			   config=dict(
 			         prediction_type=SchedulerPredictionType('v_prediction')
 					 name='stable diffusion 2 base model',
@@ -643,29 +769,38 @@ install_job = installer.import_model(
 
 This section describes additional methods provided by the installer class.
 
-#### jobs = installer.wait_for_installs()
+#### jobs = installer.wait_for_installs([timeout])
 
 Block until all pending installs are completed or errored and then
-returns a list of completed jobs.
+returns a list of completed jobs. The optional `timeout` argument will
+return from the call if jobs aren't completed in the specified
+time. An argument of 0 (the default) will block indefinitely.
 
-#### jobs = installer.list_jobs([source])
+#### jobs = installer.list_jobs()
 
-Return a list of all active and complete `ModelInstallJobs`. An
-optional `source` argument allows you to filter the returned list by a
-model source string pattern using a partial string match.
+Return a list of all active and complete `ModelInstallJobs`.
 
-#### jobs = installer.get_job(source)
+#### jobs = installer.get_job_by_source(source)
 
 Return a list of `ModelInstallJob` corresponding to the indicated
 model source.
 
+#### jobs = installer.get_job_by_id(id)
+
+Return a list of `ModelInstallJob` corresponding to the indicated
+model id.
+
+#### jobs = installer.cancel_job(job)
+
+Cancel the indicated job.
+
 #### installer.prune_jobs
 
-Remove non-pending jobs (completed or errored) from the job list
-returned by `list_jobs()` and `get_job()`.
+Remove jobs that are in a terminal state (i.e. complete, errored or
+cancelled) from the job list returned by `list_jobs()` and
+`get_job()`.
 
-#### installer.app_config, installer.record_store,
-installer.event_bus
+#### installer.app_config, installer.record_store, installer.event_bus
 
 Properties that provide access to the installer's `InvokeAIAppConfig`,
 `ModelRecordServiceBase` and `EventServiceBase` objects.
@@ -726,120 +861,6 @@ the API starts up. Its effect is to call `sync_to_config()` to
 synchronize the model record store database with what's currently on
 disk.
 
-# The remainder of this documentation is provisional, pending implementation of the Download and Load services
-
-## Let's get loaded, the lowdown on ModelLoadService
-
-The `ModelLoadService` is responsible for loading a named model into
-memory so that it can be used for inference. Despite the fact that it
-does a lot under the covers, it is very straightforward to use.
-
-An application-wide model loader is created at API initialization time
-and stored in
-`ApiDependencies.invoker.services.model_loader`. However, you can
-create alternative instances if you wish.
-
-### Creating a ModelLoadService object
-
-The class is defined in
-`invokeai.app.services.model_loader_service`. It is initialized with
-an InvokeAIAppConfig object, from which it gets configuration
-information such as the user's desired GPU and precision, and with a
-previously-created `ModelRecordServiceBase` object, from which it
-loads the requested model's configuration information.
-
-Here is a typical initialization pattern:
-
-```
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.model_record_service import ModelRecordServiceBase
-from invokeai.app.services.model_loader_service import ModelLoadService
-
-config = InvokeAIAppConfig.get_config()
-store = ModelRecordServiceBase.open(config)
-loader = ModelLoadService(config, store)
-```
-
-Note that we are relying on the contents of the application
-configuration to choose the implementation of
-`ModelRecordServiceBase`.
-
-### get_model(key, [submodel_type], [context]) -> ModelInfo:
-
-*** TO DO: change to get_model(key, context=None, **kwargs)
-
-The `get_model()` method, like its similarly-named cousin in
-`ModelRecordService`, receives the unique key that identifies the
-model.  It loads the model into memory, gets the model ready for use,
-and returns a `ModelInfo` object. 
-
-The optional second argument, `subtype` is a `SubModelType` string
-enum, such as "vae". It is mandatory when used with a main model, and
-is used to select which part of the main model to load.
-
-The optional third argument, `context` can be provided by
-an invocation to trigger model load event reporting. See below for
-details.
-
-The returned `ModelInfo` object shares some fields in common with
-`ModelConfigBase`, but is otherwise a completely different beast:
-
-| **Field Name** | **Type**        |  **Description** |
-|----------------|-----------------|------------------|
-| `key`          | str                    | The model key derived from the ModelRecordService database |
-| `name`         | str                    | Name of this model |
-| `base_model`   | BaseModelType          | Base model for this model |
-| `type`         | ModelType or SubModelType   | Either the model type (non-main) or the submodel type (main models)|
-| `location`     | Path or str            | Location of the model on the filesystem |
-| `precision`    | torch.dtype            | The torch.precision to use for inference |
-| `context`      | ModelCache.ModelLocker | A context class used to lock the model in VRAM while in use |
-
-The types for `ModelInfo` and `SubModelType` can be imported from
-`invokeai.app.services.model_loader_service`.
-
-To use the model, you use the `ModelInfo` as a context manager using
-the following pattern:
-
-```
-model_info = loader.get_model('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
-with model_info as vae:
-	image = vae.decode(latents)[0]
-```
-
-The `vae` model will stay locked in the GPU during the period of time
-it is in the context manager's scope.
-
-`get_model()` may raise any of the following exceptions:
-
-- `UnknownModelException`  -- key not in database
-- `ModelNotFoundException` -- key in database but model not found at path
-- `InvalidModelException`  -- the model is guilty of a variety of sins
-  
-** TO DO: ** Resolve discrepancy between ModelInfo.location and
-ModelConfig.path.
-
-### Emitting model loading events
-
-When the `context` argument is passed to `get_model()`, it will
-retrieve the invocation event bus from the passed `InvocationContext`
-object to emit events on the invocation bus. The two events are
-"model_load_started" and "model_load_completed". Both carry the
-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=submodel,
-	hash=model_info.hash,
-	location=str(model_info.location),
-	precision=str(model_info.precision),
-)
-```
-
 ***
 
 ## Get on line: The Download Queue
@@ -879,7 +900,6 @@ following fields:
 | `job_started`    | float            |              | Timestamp for when the job started running |
 | `job_ended`      | float            |              | Timestamp for when the job completed or errored out |
 | `job_sequence`   | int              |              | A counter that is incremented each time a model is dequeued |
-| `preserve_partial_downloads`| bool  | False        | Resume partial downloads when relaunched.   |
 | `error`          | Exception        |              | A copy of the Exception that caused an error during download |
 
 When you create a job, you can assign it a `priority`. If multiple
@@ -1184,3 +1204,362 @@ other resources that it might have been using.
 This will start/pause/cancel all jobs that have been submitted to the
 queue and have not yet reached a terminal state.
 
+***
+
+## This Meta be Good: Model Metadata Storage
+
+The modules found under `invokeai.backend.model_manager.metadata`
+provide a straightforward API for fetching model metadatda from online
+repositories. Currently two repositories are supported: HuggingFace
+and Civitai. However, the modules are easily extended for additional
+repos, provided that they have defined APIs for metadata access.
+
+Metadata comprises any descriptive information that is not essential
+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:
+
+```
+from invokeai.backend.model_manager.metadata import (
+   AnyModelRepoMetadata,
+   CivitaiMetadataFetch,
+   CivitaiMetadata
+   ModelMetadataStore,
+)
+# to access the initialized sql database
+from invokeai.app.api.dependencies import ApiDependencies
+
+civitai = CivitaiMetadataFetch()
+
+# fetch the metadata
+model_metadata = civitai.from_url("https://civitai.com/models/215796")
+
+# get some common metadata fields
+author = model_metadata.author
+tags = model_metadata.tags
+
+# get some Civitai-specific fields
+assert isinstance(model_metadata, CivitaiMetadata)
+
+trained_words = model_metadata.trained_words
+base_model = model_metadata.base_model_trained_on
+thumbnail = model_metadata.thumbnail_url
+
+# cache the metadata to the database using the key corresponding to
+# an existing model config record in the `model_config` table
+sql_cache = ModelMetadataStore(ApiDependencies.invoker.services.db)
+sql_cache.add_metadata('fb237ace520b6716adc98bcb16e8462c', model_metadata)
+
+# now we can search the database by tag, author or model name
+# matches will contain a list of model keys that match the search
+matches = sql_cache.search_by_tag({"tool", "turbo"})
+```
+
+### Structure of the Metadata objects
+
+There is a short class hierarchy of Metadata objects, all of which
+descend from the Pydantic `BaseModel`.
+
+#### `ModelMetadataBase`
+
+This is the common base class for metadata:
+
+| **Field Name** | **Type**        |  **Description** |
+|----------------|-----------------|------------------|
+| `name`        | str           | Repository's name for the model |
+| `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
+to be part of the business logic.
+
+Descendents of the base add additional fields.
+
+#### `HuggingFaceMetadata`
+
+This descends from `ModelMetadataBase` and adds the following fields:
+
+| **Field Name** | **Type**        |  **Description** |
+|----------------|-----------------|------------------|
+| `type`         | Literal["huggingface"]   | Used for the discriminated union of metadata classes|
+| `id`           | str              | HuggingFace repo_id |
+| `tag_dict`     | Dict[str, Any]   | A dictionary of tag/value pairs provided in addition to `tags` |
+| `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:
+
+| **Field Name** | **Type**        |  **Description** |
+|----------------|-----------------|------------------|
+| `type`         | Literal["civitai"]   | Used for the discriminated union of metadata classes|
+| `id`           | int                  | Civitai model id |
+| `version_name` | str                  | Name of this version of the model (distinct from model name) |
+| `version_id`   | int                  | Civitai model version id (distinct from model id) |
+| `created`      | datetime             | Date this version of the model was created |
+| `updated`      | datetime             | Date this version of the model was last updated |
+| `published`    | datetime             | Date this version of the model was published to Civitai |
+| `description`  | str                  | Model description. Quite verbose and contains HTML tags |
+| `version_description` | str           | Model version description, usually describes changes to the model |
+| `nsfw`         | bool                 | Whether the model tends to generate NSFW content |
+| `restrictions` | LicenseRestrictions  | An object that describes what is and isn't allowed with this model |
+| `trained_words`| Set[str]             | Trigger words for this model, if any |
+| `download_url` | AnyHttpUrl           | URL for downloading this version of the model |
+| `base_model_trained_on` | str         | Name of the model that this version was trained on |
+| `thumbnail_url` | AnyHttpUrl          | URL to access a representative thumbnail image of the model's output |
+| `weight_min`    | int                 | For LoRA sliders, the minimum suggested weight to apply |
+| `weight_max`    | int                 | For LoRA sliders, the maximum suggested weight to apply |
+
+Note that `weight_min` and `weight_max` are not currently populated
+and take the default values of (-1.0, +2.0). The issue is that these
+values aren't part of the structured data but appear in the text
+description. Some regular expression or LLM coding may be able to
+extract these values.
+
+Also be aware that `base_model_trained_on` is free text and doesn't
+correspond to our `ModelType` enum.
+
+`CivitaiMetadata` also defines some convenience properties relating to
+licensing restrictions: `credit_required`, `allow_commercial_use`,
+`allow_derivatives` and `allow_different_license`.
+
+#### `AnyModelRepoMetadata`
+
+This is a discriminated Union of `CivitaiMetadata` and
+`HuggingFaceMetadata`.
+
+### Fetching Metadata from Online Repos
+
+The `HuggingFaceMetadataFetch` and `CivitaiMetadataFetch` classes will
+retrieve metadata from their corresponding repositories and return
+`AnyModelRepoMetadata` objects. Their base class
+`ModelMetadataFetchBase` is an abstract class that defines two
+methods: `from_url()` and `from_id()`. The former accepts the type of
+model URLs that the user will try to cut and paste into the model
+import form. The latter accepts a string ID in the format recognized
+by the repository of choice. Both methods return an
+`AnyModelRepoMetadata`.
+
+The base class also has a class method `from_json()` which will take
+the JSON representation of a `ModelMetadata` object, validate it, and
+return the corresponding `AnyModelRepoMetadata` object.
+
+When initializing one of the metadata fetching classes, you may
+provide a `requests.Session` argument. This allows you to customize
+the low-level HTTP fetch requests and is used, for instance, in the
+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
+`HuggingFaceMetadata` object directly.
+
+#### CivitaiMetadataFetch
+
+This adds the following methods:
+
+`from_civitai_modelid()` This takes the ID of a model, finds the
+default version of the model, and then retrieves the metadata for
+that version, returning a `CivitaiMetadata` object directly.
+
+`from_civitai_versionid()` This takes the ID of a model version and
+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. 
+
+When a metadata object is saved to the database, it is identified
+using the model key, _and this key must correspond to an existing
+model key in the model_config table_. There is a foreign key integrity
+constraint between the `model_config.id` field and the
+`model_metadata.id` field such that if you attempt to save metadata
+under an unknown key, the attempt will result in an
+`UnknownModelException`. Likewise, when a model is deleted from
+`model_config`, the deletion of the corresponding metadata record will
+be triggered.
+
+Tags are stored in a normalized fashion in the tables `model_tags` and
+`tags`. Triggers keep the tag table in sync with the `model_metadata`
+table.
+
+To create the storage object, initialize it with the InvokeAI
+`SqliteDatabase` object. This is often done this way:
+
+```
+from invokeai.app.api.dependencies import ApiDependencies
+metadata_store = ModelMetadataStore(ApiDependencies.invoker.services.db)
+```
+
+You can then access the storage with the following methods:
+
+#### `add_metadata(key, metadata)`
+
+Add the metadata using a previously-defined model key.
+
+There is currently no `delete_metadata()` method. The metadata will
+persist until the matching config is deleted from the `model_config`
+table.
+
+#### `get_metadata(key) -> AnyModelRepoMetadata`
+
+Retrieve the metadata corresponding to the model key.
+
+#### `update_metadata(key, new_metadata)`
+
+Update an existing metadata record with new metadata.
+
+#### `search_by_tag(tags: Set[str]) -> Set[str]`
+
+Given a set of tags, find models that are tagged with them. If
+multiple tags are provided then a matching model must be tagged with
+*all* the tags in the set. This method returns a set of model keys and
+is intended to be used in conjunction with the `ModelRecordService`:
+
+```
+model_config_store = ApiDependencies.invoker.services.model_records
+matches = metadata_store.search_by_tag({'license:other'})
+models = [model_config_store.get(x) for x in matches]
+```
+
+#### `search_by_name(name: str) -> Set[str]
+
+Find all model metadata records that have the given name and return a
+set of keys to the corresponding model config objects.
+
+#### `search_by_author(author: str) -> Set[str]
+
+Find all model metadata records that have the given author and return
+a set of keys to the corresponding model config objects.
+
+# The remainder of this documentation is provisional, pending implementation of the Load service
+
+## Let's get loaded, the lowdown on ModelLoadService
+
+The `ModelLoadService` is responsible for loading a named model into
+memory so that it can be used for inference. Despite the fact that it
+does a lot under the covers, it is very straightforward to use.
+
+An application-wide model loader is created at API initialization time
+and stored in
+`ApiDependencies.invoker.services.model_loader`. However, you can
+create alternative instances if you wish.
+
+### Creating a ModelLoadService object
+
+The class is defined in
+`invokeai.app.services.model_loader_service`. It is initialized with
+an InvokeAIAppConfig object, from which it gets configuration
+information such as the user's desired GPU and precision, and with a
+previously-created `ModelRecordServiceBase` object, from which it
+loads the requested model's configuration information.
+
+Here is a typical initialization pattern:
+
+```
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.model_record_service import ModelRecordServiceBase
+from invokeai.app.services.model_loader_service import ModelLoadService
+
+config = InvokeAIAppConfig.get_config()
+store = ModelRecordServiceBase.open(config)
+loader = ModelLoadService(config, store)
+```
+
+Note that we are relying on the contents of the application
+configuration to choose the implementation of
+`ModelRecordServiceBase`.
+
+### get_model(key, [submodel_type], [context]) -> ModelInfo:
+
+*** TO DO: change to get_model(key, context=None, **kwargs)
+
+The `get_model()` method, like its similarly-named cousin in
+`ModelRecordService`, receives the unique key that identifies the
+model.  It loads the model into memory, gets the model ready for use,
+and returns a `ModelInfo` object. 
+
+The optional second argument, `subtype` is a `SubModelType` string
+enum, such as "vae". It is mandatory when used with a main model, and
+is used to select which part of the main model to load.
+
+The optional third argument, `context` can be provided by
+an invocation to trigger model load event reporting. See below for
+details.
+
+The returned `ModelInfo` object shares some fields in common with
+`ModelConfigBase`, but is otherwise a completely different beast:
+
+| **Field Name** | **Type**        |  **Description** |
+|----------------|-----------------|------------------|
+| `key`          | str                    | The model key derived from the ModelRecordService database |
+| `name`         | str                    | Name of this model |
+| `base_model`   | BaseModelType          | Base model for this model |
+| `type`         | ModelType or SubModelType   | Either the model type (non-main) or the submodel type (main models)|
+| `location`     | Path or str            | Location of the model on the filesystem |
+| `precision`    | torch.dtype            | The torch.precision to use for inference |
+| `context`      | ModelCache.ModelLocker | A context class used to lock the model in VRAM while in use |
+
+The types for `ModelInfo` and `SubModelType` can be imported from
+`invokeai.app.services.model_loader_service`.
+
+To use the model, you use the `ModelInfo` as a context manager using
+the following pattern:
+
+```
+model_info = loader.get_model('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
+with model_info as vae:
+	image = vae.decode(latents)[0]
+```
+
+The `vae` model will stay locked in the GPU during the period of time
+it is in the context manager's scope.
+
+`get_model()` may raise any of the following exceptions:
+
+- `UnknownModelException`  -- key not in database
+- `ModelNotFoundException` -- key in database but model not found at path
+- `InvalidModelException`  -- the model is guilty of a variety of sins
+  
+** TO DO: ** Resolve discrepancy between ModelInfo.location and
+ModelConfig.path.
+
+### Emitting model loading events
+
+When the `context` argument is passed to `get_model()`, it will
+retrieve the invocation event bus from the passed `InvocationContext`
+object to emit events on the invocation bus. The two events are
+"model_load_started" and "model_load_completed". Both carry the
+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=submodel,
+	hash=model_info.hash,
+	location=str(model_info.location),
+	precision=str(model_info.precision),
+)
+```
+

docs/nodes/communityNodes.md

@@ -25,7 +25,6 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
     + [Grid to Gif](#grid-to-gif)
     + [Halftone](#halftone)
-    + [Ideal Size](#ideal-size)
     + [Image and Mask Composition Pack](#image-and-mask-composition-pack)
     + [Image Dominant Color](#image-dominant-color)
     + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
@@ -196,13 +195,6 @@ CMYK Halftone Output:
 
 <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
 
---------------------------------
-### Ideal Size
-
-**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
-
-**Node Link:** https://github.com/JPPhoto/ideal-size-node
-
 --------------------------------
 ### Image and Mask Composition Pack
 

docs/nodes/defaultNodes.md

@@ -36,6 +36,7 @@ their descriptions.
 | Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
 | Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
 | Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
+| Ideal Size                                                    | Calculates an ideal image size for latents for a first pass of a multi-pass upscaling to avoid duplication and other artifacts                       |
 | Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
 | Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
 | Image Primitive                                               | An image primitive value                                                                                                                             |

invokeai/app/api/dependencies.py

@@ -3,6 +3,7 @@
 from logging import Logger
 
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
+from invokeai.backend.model_manager.metadata import ModelMetadataStore
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -61,7 +62,7 @@ class ApiDependencies:
     invoker: Invoker
 
     @staticmethod
-    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
+    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger) -> None:
         logger.info(f"InvokeAI version {__version__}")
         logger.info(f"Root directory = {str(config.root_path)}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
@@ -87,8 +88,13 @@ class ApiDependencies:
         model_manager = ModelManagerService(config, logger)
         model_record_service = ModelRecordServiceSQL(db=db)
         download_queue_service = DownloadQueueService(event_bus=events)
+        metadata_store = ModelMetadataStore(db=db)
         model_install_service = ModelInstallService(
-            app_config=config, record_store=model_record_service, event_bus=events
+            app_config=config,
+            record_store=model_record_service,
+            download_queue=download_queue_service,
+            metadata_store=metadata_store,
+            event_bus=events,
         )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
@@ -131,6 +137,6 @@ class ApiDependencies:
         db.clean()
 
     @staticmethod
-    def shutdown():
+    def shutdown() -> None:
         if ApiDependencies.invoker:
             ApiDependencies.invoker.stop()

invokeai/app/api/no_cache_staticfiles.py

@@ -0,0 +1,28 @@
+from typing import Any
+
+from starlette.responses import Response
+from starlette.staticfiles import StaticFiles
+
+
+class NoCacheStaticFiles(StaticFiles):
+    """
+    This class is used to override the default caching behavior of starlette for static files,
+    ensuring we *never* cache static files. It modifies the file response headers to strictly
+    never cache the files.
+
+    Static files include the javascript bundles, fonts, locales, and some images. Generated
+    images are not included, as they are served by a router.
+    """
+
+    def __init__(self, *args: Any, **kwargs: Any):
+        self.cachecontrol = "max-age=0, no-cache, no-store, , must-revalidate"
+        self.pragma = "no-cache"
+        self.expires = "0"
+        super().__init__(*args, **kwargs)
+
+    def file_response(self, *args: Any, **kwargs: Any) -> Response:
+        resp = super().file_response(*args, **kwargs)
+        resp.headers.setdefault("Cache-Control", self.cachecontrol)
+        resp.headers.setdefault("Pragma", self.pragma)
+        resp.headers.setdefault("Expires", self.expires)
+        return resp

invokeai/app/api/routers/download_queue.py

@@ -24,6 +24,7 @@ download_queue_router = APIRouter(prefix="/v1/download_queue", tags=["download_q
 )
 async def list_downloads() -> List[DownloadJob]:
     """Get a list of active and inactive jobs."""
+    print("test")
     queue = ApiDependencies.invoker.services.download_queue
     return queue.list_jobs()
 

invokeai/app/api/routers/model_records.py

@@ -4,7 +4,7 @@
 
 from hashlib import sha1
 from random import randbytes
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Set
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
@@ -16,13 +16,18 @@ from invokeai.app.services.model_install import ModelInstallJob, ModelSource
 from invokeai.app.services.model_records import (
     DuplicateModelException,
     InvalidModelException,
+    ModelRecordOrderBy,
+    ModelSummary,
     UnknownModelException,
 )
+from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
 
 from ..dependencies import ApiDependencies
 
@@ -32,11 +37,20 @@ model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager
 class ModelsList(BaseModel):
     """Return list of configs."""
 
-    models: list[AnyModelConfig]
+    models: List[AnyModelConfig]
 
     model_config = ConfigDict(use_enum_values=True)
 
 
+class ModelTagSet(BaseModel):
+    """Return tags for a set of models."""
+
+    key: str
+    name: str
+    author: str
+    tags: Set[str]
+
+
 @model_records_router.get(
     "/",
     operation_id="list_model_records",
@@ -45,7 +59,7 @@ async def list_model_records(
     base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
     model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
     model_name: Optional[str] = Query(default=None, description="Exact match on the name of the model"),
-    model_format: Optional[str] = Query(
+    model_format: Optional[ModelFormat] = Query(
         default=None, description="Exact match on the format of the model (e.g. 'diffusers')"
     ),
 ) -> ModelsList:
@@ -86,6 +100,59 @@ async def get_model_record(
         raise HTTPException(status_code=404, detail=str(e))
 
 
+@model_records_router.get("/meta", operation_id="list_model_summary")
+async def list_model_summary(
+    page: int = Query(default=0, description="The page to get"),
+    per_page: int = Query(default=10, description="The number of models per page"),
+    order_by: ModelRecordOrderBy = Query(default=ModelRecordOrderBy.Default, description="The attribute to order by"),
+) -> PaginatedResults[ModelSummary]:
+    """Gets a page of model summary data."""
+    return ApiDependencies.invoker.services.model_records.list_models(page=page, per_page=per_page, order_by=order_by)
+
+
+@model_records_router.get(
+    "/meta/i/{key}",
+    operation_id="get_model_metadata",
+    responses={
+        200: {"description": "Success"},
+        400: {"description": "Bad request"},
+        404: {"description": "No metadata available"},
+    },
+)
+async def get_model_metadata(
+    key: str = Path(description="Key of the model repo metadata to fetch."),
+) -> Optional[AnyModelRepoMetadata]:
+    """Get a model metadata object."""
+    record_store = ApiDependencies.invoker.services.model_records
+    result = record_store.get_metadata(key)
+    if not result:
+        raise HTTPException(status_code=404, detail="No metadata for a model with this key")
+    return result
+
+
+@model_records_router.get(
+    "/tags",
+    operation_id="list_tags",
+)
+async def list_tags() -> Set[str]:
+    """Get a unique set of all the model tags."""
+    record_store = ApiDependencies.invoker.services.model_records
+    return record_store.list_tags()
+
+
+@model_records_router.get(
+    "/tags/search",
+    operation_id="search_by_metadata_tags",
+)
+async def search_by_metadata_tags(
+    tags: Set[str] = Query(default=None, description="Tags to search for"),
+) -> ModelsList:
+    """Get a list of models."""
+    record_store = ApiDependencies.invoker.services.model_records
+    results = record_store.search_by_metadata_tag(tags)
+    return ModelsList(models=results)
+
+
 @model_records_router.patch(
     "/i/{key}",
     operation_id="update_model_record",
@@ -159,9 +226,7 @@ async def del_model_record(
 async def add_model_record(
     config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
 ) -> AnyModelConfig:
-    """
-    Add a model using the configuration information appropriate for its type.
-    """
+    """Add a model using the configuration information appropriate for its type."""
     logger = ApiDependencies.invoker.services.logger
     record_store = ApiDependencies.invoker.services.model_records
     if config.key == "<NOKEY>":
@@ -243,7 +308,7 @@ async def import_model(
     Installation occurs in the background. Either use list_model_install_jobs()
     to poll for completion, or listen on the event bus for the following events:
 
-      "model_install_started"
+      "model_install_running"
       "model_install_completed"
       "model_install_error"
 
@@ -279,16 +344,46 @@ async def import_model(
     operation_id="list_model_install_jobs",
 )
 async def list_model_install_jobs() -> List[ModelInstallJob]:
-    """
-    Return list of model install jobs.
-
-    If the optional 'source' argument is provided, then the list will be filtered
-    for partial string matches against the install source.
-    """
+    """Return list of model install jobs."""
     jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_install.list_jobs()
     return jobs
 
 
+@model_records_router.get(
+    "/import/{id}",
+    operation_id="get_model_install_job",
+    responses={
+        200: {"description": "Success"},
+        404: {"description": "No such job"},
+    },
+)
+async def get_model_install_job(id: int = Path(description="Model install id")) -> ModelInstallJob:
+    """Return model install job corresponding to the given source."""
+    try:
+        return ApiDependencies.invoker.services.model_install.get_job_by_id(id)
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@model_records_router.delete(
+    "/import/{id}",
+    operation_id="cancel_model_install_job",
+    responses={
+        201: {"description": "The job was cancelled successfully"},
+        415: {"description": "No such job"},
+    },
+    status_code=201,
+)
+async def cancel_model_install_job(id: int = Path(description="Model install job ID")) -> None:
+    """Cancel the model install job(s) corresponding to the given job ID."""
+    installer = ApiDependencies.invoker.services.model_install
+    try:
+        job = installer.get_job_by_id(id)
+    except ValueError as e:
+        raise HTTPException(status_code=415, detail=str(e))
+    installer.cancel_job(job)
+
+
 @model_records_router.patch(
     "/import",
     operation_id="prune_model_install_jobs",
@@ -298,9 +393,7 @@ async def list_model_install_jobs() -> List[ModelInstallJob]:
     },
 )
 async def prune_model_install_jobs() -> Response:
-    """
-    Prune all completed and errored jobs from the install job list.
-    """
+    """Prune all completed and errored jobs from the install job list."""
     ApiDependencies.invoker.services.model_install.prune_jobs()
     return Response(status_code=204)
 
@@ -315,7 +408,9 @@ async def prune_model_install_jobs() -> Response:
 )
 async def sync_models_to_config() -> Response:
     """
-    Traverse the models and autoimport directories. Model files without a corresponding
+    Traverse the models and autoimport directories.
+
+    Model files without a corresponding
     record in the database are added. Orphan records without a models file are deleted.
     """
     ApiDependencies.invoker.services.model_install.sync_to_config()

invokeai/app/api_app.py

@@ -3,6 +3,7 @@
 # values from the command line or config file.
 import sys
 
+from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.version.invokeai_version import __version__
 
 from .services.config import InvokeAIAppConfig
@@ -27,8 +28,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     from fastapi.middleware.gzip import GZipMiddleware
     from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
     from fastapi.openapi.utils import get_openapi
-    from fastapi.responses import FileResponse, HTMLResponse
-    from fastapi.staticfiles import StaticFiles
+    from fastapi.responses import HTMLResponse
     from fastapi_events.handlers.local import local_handler
     from fastapi_events.middleware import EventHandlerASGIMiddleware
     from pydantic.json_schema import models_json_schema
@@ -221,19 +221,13 @@ def overridden_redoc() -> HTMLResponse:
 
 web_root_path = Path(list(web_dir.__path__)[0])
 
-# Only serve the UI if we it has a build
-if (web_root_path / "dist").exists():
-    # Cannot add headers to StaticFiles, so we must serve index.html with a custom route
-    # Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
-    @app.get("/", include_in_schema=False, name="ui_root")
-    def get_index() -> FileResponse:
-        return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
-
-    # Must mount *after* the other routes else it borks em
-    app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
-    app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
-
-app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static")  # docs favicon is in here
+try:
+    app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
+except RuntimeError:
+    logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
+app.mount(
+    "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
+)  # docs favicon is in here
 
 
 def invoke_api() -> None:

invokeai/app/invocations/latent.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
+import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
@@ -74,6 +75,8 @@ from .model import ModelInfo, UNetField, VaeField
 if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
+print("test")
+
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
@@ -1228,3 +1231,57 @@ class CropLatentsCoreInvocation(BaseInvocation):
         context.services.latents.save(name, cropped_latents)
 
         return build_latents_output(latents_name=name, latents=cropped_latents)
+
+
+@invocation_output("ideal_size_output")
+class IdealSizeOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    width: int = OutputField(description="The ideal width of the image (in pixels)")
+    height: int = OutputField(description="The ideal height of the image (in pixels)")
+
+
+@invocation(
+    "ideal_size",
+    title="Ideal Size",
+    tags=["latents", "math", "ideal_size"],
+    version="1.0.2",
+)
+class IdealSizeInvocation(BaseInvocation):
+    """Calculates the ideal size for generation to avoid duplication"""
+
+    width: int = InputField(default=1024, description="Final image width")
+    height: int = InputField(default=576, description="Final image height")
+    unet: UNetField = InputField(default=None, description=FieldDescriptions.unet)
+    multiplier: float = InputField(
+        default=1.0,
+        description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in initial generation artifacts if too large)",
+    )
+
+    def trim_to_multiple_of(self, *args, multiple_of=LATENT_SCALE_FACTOR):
+        return tuple((x - x % multiple_of) for x in args)
+
+    def invoke(self, context: InvocationContext) -> IdealSizeOutput:
+        aspect = self.width / self.height
+        dimension = 512
+        if self.unet.unet.base_model == BaseModelType.StableDiffusion2:
+            dimension = 768
+        elif self.unet.unet.base_model == BaseModelType.StableDiffusionXL:
+            dimension = 1024
+        dimension = dimension * self.multiplier
+        min_dimension = math.floor(dimension * 0.5)
+        model_area = dimension * dimension  # hardcoded for now since all models are trained on square images
+
+        if aspect > 1.0:
+            init_height = max(min_dimension, math.sqrt(model_area / aspect))
+            init_width = init_height * aspect
+        else:
+            init_width = max(min_dimension, math.sqrt(model_area * aspect))
+            init_height = init_width / aspect
+
+        scaled_width, scaled_height = self.trim_to_multiple_of(
+            math.floor(init_width),
+            math.floor(init_height),
+        )
+
+        return IdealSizeOutput(width=scaled_width, height=scaled_height)

invokeai/app/services/config/config_default.py

@@ -209,7 +209,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     """Configuration object for InvokeAI App."""
 
     singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
-    singleton_init: ClassVar[Optional[Dict]] = None
+    singleton_init: ClassVar[Optional[Dict[str, Any]]] = None
 
     # fmt: off
     type: Literal["InvokeAI"] = "InvokeAI"
@@ -263,7 +263,7 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # DEVICE
     device              : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
-    precision           : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
+    precision           : Literal["auto", "float16", "bfloat16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
 
     # GENERATION
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", json_schema_extra=Categories.Generation)
@@ -301,8 +301,8 @@ class InvokeAIAppConfig(InvokeAISettings):
         self,
         argv: Optional[list[str]] = None,
         conf: Optional[DictConfig] = None,
-        clobber=False,
-    ):
+        clobber: Optional[bool] = False,
+    ) -> None:
         """
         Update settings with contents of init file, environment, and command-line settings.
 
@@ -337,7 +337,7 @@ class InvokeAIAppConfig(InvokeAISettings):
                 )
 
     @classmethod
-    def get_config(cls, **kwargs: Dict[str, Any]) -> InvokeAIAppConfig:
+    def get_config(cls, **kwargs: Any) -> InvokeAIAppConfig:
         """Return a singleton InvokeAIAppConfig configuration object."""
         if (
             cls.singleton_config is None
@@ -455,7 +455,7 @@ class InvokeAIAppConfig(InvokeAISettings):
         return _find_root()
 
 
-def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
+def get_invokeai_config(**kwargs: Any) -> InvokeAIAppConfig:
     """Legacy function which returns InvokeAIAppConfig.get_config()."""
     return InvokeAIAppConfig.get_config(**kwargs)
 

invokeai/app/services/download/download_base.py

@@ -34,6 +34,7 @@ class ServiceInactiveException(Exception):
 
 
 DownloadEventHandler = Callable[["DownloadJob"], None]
+DownloadExceptionHandler = Callable[["DownloadJob", Optional[Exception]], None]
 
 
 @total_ordering
@@ -55,6 +56,7 @@ class DownloadJob(BaseModel):
     job_ended: Optional[str] = Field(
         default=None, description="Timestamp for when the download job ende1d (completed or errored)"
     )
+    content_type: Optional[str] = Field(default=None, description="Content type of downloaded file")
     bytes: int = Field(default=0, description="Bytes downloaded so far")
     total_bytes: int = Field(default=0, description="Total file size (bytes)")
 
@@ -70,7 +72,11 @@ class DownloadJob(BaseModel):
     _on_progress: Optional[DownloadEventHandler] = PrivateAttr(default=None)
     _on_complete: Optional[DownloadEventHandler] = PrivateAttr(default=None)
     _on_cancelled: Optional[DownloadEventHandler] = PrivateAttr(default=None)
-    _on_error: Optional[DownloadEventHandler] = PrivateAttr(default=None)
+    _on_error: Optional[DownloadExceptionHandler] = PrivateAttr(default=None)
+
+    def __hash__(self) -> int:
+        """Return hash of the string representation of this object, for indexing."""
+        return hash(str(self))
 
     def __le__(self, other: "DownloadJob") -> bool:
         """Return True if this job's priority is less than another's."""
@@ -87,6 +93,26 @@ class DownloadJob(BaseModel):
         """Call to cancel the job."""
         return self._cancelled
 
+    @property
+    def complete(self) -> bool:
+        """Return true if job completed without errors."""
+        return self.status == DownloadJobStatus.COMPLETED
+
+    @property
+    def running(self) -> bool:
+        """Return true if the job is running."""
+        return self.status == DownloadJobStatus.RUNNING
+
+    @property
+    def errored(self) -> bool:
+        """Return true if the job is errored."""
+        return self.status == DownloadJobStatus.ERROR
+
+    @property
+    def in_terminal_state(self) -> bool:
+        """Return true if job has finished, one way or another."""
+        return self.status not in [DownloadJobStatus.WAITING, DownloadJobStatus.RUNNING]
+
     @property
     def on_start(self) -> Optional[DownloadEventHandler]:
         """Return the on_start event handler."""
@@ -103,7 +129,7 @@ class DownloadJob(BaseModel):
         return self._on_complete
 
     @property
-    def on_error(self) -> Optional[DownloadEventHandler]:
+    def on_error(self) -> Optional[DownloadExceptionHandler]:
         """Return the on_error event handler."""
         return self._on_error
 
@@ -118,7 +144,7 @@ class DownloadJob(BaseModel):
         on_progress: Optional[DownloadEventHandler] = None,
         on_complete: Optional[DownloadEventHandler] = None,
         on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
     ) -> None:
         """Set the callbacks for download events."""
         self._on_start = on_start
@@ -150,10 +176,10 @@ class DownloadQueueServiceBase(ABC):
         on_progress: Optional[DownloadEventHandler] = None,
         on_complete: Optional[DownloadEventHandler] = None,
         on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
     ) -> DownloadJob:
         """
-        Create a download job.
+        Create and enqueue download job.
 
         :param source: Source of the download as a URL.
         :param dest: Path to download to. See below.
@@ -175,6 +201,25 @@ class DownloadQueueServiceBase(ABC):
         """
         pass
 
+    @abstractmethod
+    def submit_download_job(
+        self,
+        job: DownloadJob,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
+    ) -> None:
+        """
+        Enqueue a download job.
+
+        :param job: The DownloadJob
+        :param on_start, on_progress, on_complete, on_error: Callbacks for the indicated
+         events.
+        """
+        pass
+
     @abstractmethod
     def list_jobs(self) -> List[DownloadJob]:
         """
@@ -197,21 +242,21 @@ class DownloadQueueServiceBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_all_jobs(self):
+    def cancel_all_jobs(self) -> None:
         """Cancel all active and enquedjobs."""
         pass
 
     @abstractmethod
-    def prune_jobs(self):
+    def prune_jobs(self) -> None:
         """Prune completed and errored queue items from the job list."""
         pass
 
     @abstractmethod
-    def cancel_job(self, job: DownloadJob):
+    def cancel_job(self, job: DownloadJob) -> None:
         """Cancel the job, clearing partial downloads and putting it into ERROR state."""
         pass
 
     @abstractmethod
-    def join(self):
+    def join(self) -> None:
         """Wait until all jobs are off the queue."""
         pass

invokeai/app/services/download/download_default.py

@@ -5,10 +5,9 @@ import os
 import re
 import threading
 import traceback
-from logging import Logger
 from pathlib import Path
 from queue import Empty, PriorityQueue
-from typing import Any, Dict, List, Optional, Set
+from typing import Any, Dict, List, Optional
 
 import requests
 from pydantic.networks import AnyHttpUrl
@@ -21,6 +20,7 @@ from invokeai.backend.util.logging import InvokeAILogger
 
 from .download_base import (
     DownloadEventHandler,
+    DownloadExceptionHandler,
     DownloadJob,
     DownloadJobCancelledException,
     DownloadJobStatus,
@@ -36,18 +36,6 @@ DOWNLOAD_CHUNK_SIZE = 100000
 class DownloadQueueService(DownloadQueueServiceBase):
     """Class for queued download of models."""
 
-    _jobs: Dict[int, DownloadJob]
-    _max_parallel_dl: int = 5
-    _worker_pool: Set[threading.Thread]
-    _queue: PriorityQueue[DownloadJob]
-    _stop_event: threading.Event
-    _lock: threading.Lock
-    _logger: Logger
-    _events: Optional[EventServiceBase] = None
-    _next_job_id: int = 0
-    _accept_download_requests: bool = False
-    _requests: requests.sessions.Session
-
     def __init__(
         self,
         max_parallel_dl: int = 5,
@@ -99,6 +87,33 @@ class DownloadQueueService(DownloadQueueServiceBase):
             self._stop_event.set()
             self._worker_pool.clear()
 
+    def submit_download_job(
+        self,
+        job: DownloadJob,
+        on_start: Optional[DownloadEventHandler] = None,
+        on_progress: Optional[DownloadEventHandler] = None,
+        on_complete: Optional[DownloadEventHandler] = None,
+        on_cancelled: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
+    ) -> None:
+        """Enqueue a download job."""
+        if not self._accept_download_requests:
+            raise ServiceInactiveException(
+                "The download service is not currently accepting requests. Please call start() to initialize the service."
+            )
+        with self._lock:
+            job.id = self._next_job_id
+            self._next_job_id += 1
+            job.set_callbacks(
+                on_start=on_start,
+                on_progress=on_progress,
+                on_complete=on_complete,
+                on_cancelled=on_cancelled,
+                on_error=on_error,
+            )
+            self._jobs[job.id] = job
+            self._queue.put(job)
+
     def download(
         self,
         source: AnyHttpUrl,
@@ -109,32 +124,27 @@ class DownloadQueueService(DownloadQueueServiceBase):
         on_progress: Optional[DownloadEventHandler] = None,
         on_complete: Optional[DownloadEventHandler] = None,
         on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadEventHandler] = None,
+        on_error: Optional[DownloadExceptionHandler] = None,
     ) -> DownloadJob:
-        """Create a download job and return its ID."""
+        """Create and enqueue a download job and return it."""
         if not self._accept_download_requests:
             raise ServiceInactiveException(
                 "The download service is not currently accepting requests. Please call start() to initialize the service."
             )
-        with self._lock:
-            id = self._next_job_id
-            self._next_job_id += 1
-            job = DownloadJob(
-                id=id,
-                source=source,
-                dest=dest,
-                priority=priority,
-                access_token=access_token,
-            )
-            job.set_callbacks(
-                on_start=on_start,
-                on_progress=on_progress,
-                on_complete=on_complete,
-                on_cancelled=on_cancelled,
-                on_error=on_error,
-            )
-            self._jobs[id] = job
-            self._queue.put(job)
+        job = DownloadJob(
+            source=source,
+            dest=dest,
+            priority=priority,
+            access_token=access_token,
+        )
+        self.submit_download_job(
+            job,
+            on_start=on_start,
+            on_progress=on_progress,
+            on_complete=on_complete,
+            on_cancelled=on_cancelled,
+            on_error=on_error,
+        )
         return job
 
     def join(self) -> None:
@@ -150,7 +160,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
         with self._lock:
             to_delete = set()
             for job_id, job in self._jobs.items():
-                if self._in_terminal_state(job):
+                if job.in_terminal_state:
                     to_delete.add(job_id)
             for job_id in to_delete:
                 del self._jobs[job_id]
@@ -172,19 +182,12 @@ class DownloadQueueService(DownloadQueueServiceBase):
         with self._lock:
             job.cancel()
 
-    def cancel_all_jobs(self, preserve_partial: bool = False) -> None:
+    def cancel_all_jobs(self) -> None:
         """Cancel all jobs (those not in enqueued, running or paused state)."""
         for job in self._jobs.values():
-            if not self._in_terminal_state(job):
+            if not job.in_terminal_state:
                 self.cancel_job(job)
 
-    def _in_terminal_state(self, job: DownloadJob) -> bool:
-        return job.status in [
-            DownloadJobStatus.COMPLETED,
-            DownloadJobStatus.CANCELLED,
-            DownloadJobStatus.ERROR,
-        ]
-
     def _start_workers(self, max_workers: int) -> None:
         """Start the requested number of worker threads."""
         self._stop_event.clear()
@@ -214,7 +217,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
             except (OSError, HTTPError) as excp:
                 job.error_type = excp.__class__.__name__ + f"({str(excp)})"
                 job.error = traceback.format_exc()
-                self._signal_job_error(job)
+                self._signal_job_error(job, excp)
             except DownloadJobCancelledException:
                 self._signal_job_cancelled(job)
                 self._cleanup_cancelled_job(job)
@@ -235,6 +238,8 @@ class DownloadQueueService(DownloadQueueServiceBase):
         resp = self._requests.get(str(url), headers=header, stream=True)
         if not resp.ok:
             raise HTTPError(resp.reason)
+
+        job.content_type = resp.headers.get("Content-Type")
         content_length = int(resp.headers.get("content-length", 0))
         job.total_bytes = content_length
 
@@ -296,6 +301,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
                     self._signal_job_progress(job)
 
         # if we get here we are done and can rename the file to the original dest
+        self._logger.debug(f"{job.source}: saved to {job.download_path} (bytes={job.bytes})")
         in_progress_path.rename(job.download_path)
 
     def _validate_filename(self, directory: str, filename: str) -> bool:
@@ -322,7 +328,9 @@ class DownloadQueueService(DownloadQueueServiceBase):
             try:
                 job.on_start(job)
             except Exception as e:
-                self._logger.error(e)
+                self._logger.error(
+                    f"An error occurred while processing the on_start callback: {traceback.format_exception(e)}"
+                )
         if self._event_bus:
             assert job.download_path
             self._event_bus.emit_download_started(str(job.source), job.download_path.as_posix())
@@ -332,7 +340,9 @@ class DownloadQueueService(DownloadQueueServiceBase):
             try:
                 job.on_progress(job)
             except Exception as e:
-                self._logger.error(e)
+                self._logger.error(
+                    f"An error occurred while processing the on_progress callback: {traceback.format_exception(e)}"
+                )
         if self._event_bus:
             assert job.download_path
             self._event_bus.emit_download_progress(
@@ -348,7 +358,9 @@ class DownloadQueueService(DownloadQueueServiceBase):
             try:
                 job.on_complete(job)
             except Exception as e:
-                self._logger.error(e)
+                self._logger.error(
+                    f"An error occurred while processing the on_complete callback: {traceback.format_exception(e)}"
+                )
         if self._event_bus:
             assert job.download_path
             self._event_bus.emit_download_complete(
@@ -356,29 +368,36 @@ class DownloadQueueService(DownloadQueueServiceBase):
             )
 
     def _signal_job_cancelled(self, job: DownloadJob) -> None:
+        if job.status not in [DownloadJobStatus.RUNNING, DownloadJobStatus.WAITING]:
+            return
         job.status = DownloadJobStatus.CANCELLED
         if job.on_cancelled:
             try:
                 job.on_cancelled(job)
             except Exception as e:
-                self._logger.error(e)
+                self._logger.error(
+                    f"An error occurred while processing the on_cancelled callback: {traceback.format_exception(e)}"
+                )
         if self._event_bus:
             self._event_bus.emit_download_cancelled(str(job.source))
 
-    def _signal_job_error(self, job: DownloadJob) -> None:
+    def _signal_job_error(self, job: DownloadJob, excp: Optional[Exception] = None) -> None:
         job.status = DownloadJobStatus.ERROR
+        self._logger.error(f"{str(job.source)}: {traceback.format_exception(excp)}")
         if job.on_error:
             try:
-                job.on_error(job)
+                job.on_error(job, excp)
             except Exception as e:
-                self._logger.error(e)
+                self._logger.error(
+                    f"An error occurred while processing the on_error callback: {traceback.format_exception(e)}"
+                )
         if self._event_bus:
             assert job.error_type
             assert job.error
             self._event_bus.emit_download_error(str(job.source), error_type=job.error_type, error=job.error)
 
     def _cleanup_cancelled_job(self, job: DownloadJob) -> None:
-        self._logger.warning(f"Cleaning up leftover files from cancelled download job {job.download_path}")
+        self._logger.debug(f"Cleaning up leftover files from cancelled download job {job.download_path}")
         try:
             if job.download_path:
                 partial_file = self._in_progress_path(job.download_path)

invokeai/app/services/events/events_base.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 
-from typing import Any, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
 from invokeai.app.services.session_queue.session_queue_common import (
@@ -404,53 +404,72 @@ class EventServiceBase:
             },
         )
 
-    def emit_model_install_started(self, source: str) -> None:
+    def emit_model_install_downloading(
+        self,
+        source: str,
+        local_path: str,
+        bytes: int,
+        total_bytes: int,
+        parts: List[Dict[str, Union[str, int]]],
+    ) -> None:
         """
-        Emitted when an install job is started.
+        Emit at intervals while the install job is in progress (remote models only).
+
+        :param source: Source of the model
+        :param local_path: Where model is downloading to
+        :param parts: Progress of downloading URLs that comprise the model, if any.
+        :param bytes: Number of bytes downloaded so far.
+        :param total_bytes: Total size of download, including all files.
+        This emits a Dict with keys "source", "local_path", "bytes" and "total_bytes".
+        """
+        self.__emit_model_event(
+            event_name="model_install_downloading",
+            payload={
+                "source": source,
+                "local_path": local_path,
+                "bytes": bytes,
+                "total_bytes": total_bytes,
+                "parts": parts,
+            },
+        )
+
+    def emit_model_install_running(self, source: str) -> None:
+        """
+        Emit once when an install job becomes active.
 
         :param source: Source of the model; local path, repo_id or url
         """
         self.__emit_model_event(
-            event_name="model_install_started",
+            event_name="model_install_running",
             payload={"source": source},
         )
 
-    def emit_model_install_completed(self, source: str, key: str) -> None:
+    def emit_model_install_completed(self, source: str, key: str, total_bytes: Optional[int] = None) -> None:
         """
-        Emitted when an install job is completed successfully.
+        Emit when an install job is completed successfully.
 
         :param source: Source of the model; local path, repo_id or url
         :param key: Model config record key
+        :param total_bytes: Size of the model (may be None for installation of a local path)
         """
         self.__emit_model_event(
             event_name="model_install_completed",
             payload={
                 "source": source,
+                "total_bytes": total_bytes,
                 "key": key,
             },
         )
 
-    def emit_model_install_progress(
-        self,
-        source: str,
-        current_bytes: int,
-        total_bytes: int,
-    ) -> None:
+    def emit_model_install_cancelled(self, source: str) -> None:
         """
-        Emitted while the install job is in progress.
-        (Downloaded models only)
+        Emit when an install job is cancelled.
 
-        :param source: Source of the model
-        :param current_bytes: Number of bytes downloaded so far
-        :param total_bytes: Total bytes to download
+        :param source: Source of the model; local path, repo_id or url
         """
         self.__emit_model_event(
-            event_name="model_install_progress",
-            payload={
-                "source": source,
-                "current_bytes": int,
-                "total_bytes": int,
-            },
+            event_name="model_install_cancelled",
+            payload={"source": source},
         )
 
     def emit_model_install_error(
@@ -460,10 +479,11 @@ class EventServiceBase:
         error: str,
     ) -> None:
         """
-        Emitted when an install job encounters an exception.
+        Emit when an install job encounters an exception.
 
         :param source: Source of the model
-        :param exception: The exception that raised the error
+        :param error_type: The name of the exception
+        :param error: A text description of the exception
         """
         self.__emit_model_event(
             event_name="model_install_error",

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -23,6 +23,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
         self.__threadLimit = BoundedSemaphore(1)
         self.__invoker = invoker
         self.__stop_event = Event()
+        print("test")
         self.__invoker_thread = Thread(
             name="invoker_processor",
             target=self.__process,
@@ -132,7 +133,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             source_node_id=source_node_id,
                             result=outputs.model_dump(),
                         )
-                    self.__invoker.services.performance_statistics.log_stats()
 
                 except KeyboardInterrupt:
                     pass
@@ -195,6 +195,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             error=traceback.format_exc(),
                         )
                 elif is_complete:
+                    self.__invoker.services.performance_statistics.log_stats(graph_execution_state.id)
                     self.__invoker.services.events.emit_graph_execution_complete(
                         queue_batch_id=queue_item.session_queue_batch_id,
                         queue_item_id=queue_item.session_queue_item_id,

invokeai/app/services/invocation_stats/invocation_stats_base.py

@@ -30,23 +30,13 @@ writes to the system log is stored in InvocationServices.performance_statistics.
 
 from abc import ABC, abstractmethod
 from contextlib import AbstractContextManager
-from typing import Dict
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
-from invokeai.backend.model_management.model_cache import CacheStats
-
-from .invocation_stats_common import NodeLog
 
 
 class InvocationStatsServiceBase(ABC):
     "Abstract base class for recording node memory/time performance statistics"
 
-    # {graph_id => NodeLog}
-    _stats: Dict[str, NodeLog]
-    _cache_stats: Dict[str, CacheStats]
-    ram_used: float
-    ram_changed: float
-
     @abstractmethod
     def __init__(self):
         """
@@ -77,45 +67,8 @@ class InvocationStatsServiceBase(ABC):
         pass
 
     @abstractmethod
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        pass
-
-    @abstractmethod
-    def update_invocation_stats(
-        self,
-        graph_id: str,
-        invocation_type: str,
-        time_used: float,
-        vram_used: float,
-    ):
-        """
-        Add timing information on execution of a node. Usually
-        used internally.
-        :param graph_id: ID of the graph that is currently executing
-        :param invocation_type: String literal type of the node
-        :param time_used: Time used by node's exection (sec)
-        :param vram_used: Maximum VRAM used during exection (GB)
-        """
-        pass
-
-    @abstractmethod
-    def log_stats(self):
+    def log_stats(self, graph_execution_state_id: str):
         """
         Write out the accumulated statistics to the log or somewhere else.
         """
         pass
-
-    @abstractmethod
-    def update_mem_stats(
-        self,
-        ram_used: float,
-        ram_changed: float,
-    ):
-        """
-        Update the collector with RAM memory usage info.
-
-        :param ram_used: How much RAM is currently in use.
-        :param ram_changed: How much RAM changed since last generation.
-        """
-        pass

invokeai/app/services/invocation_stats/invocation_stats_common.py

@@ -1,25 +1,84 @@
-from dataclasses import dataclass, field
-from typing import Dict
-
-# size of GIG in bytes
-GIG = 1073741824
+from collections import defaultdict
+from dataclasses import dataclass
 
 
 @dataclass
-class NodeStats:
-    """Class for tracking execution stats of an invocation node"""
+class NodeExecutionStats:
+    """Class for tracking execution stats of an invocation node."""
 
-    calls: int = 0
-    time_used: float = 0.0  # seconds
-    max_vram: float = 0.0  # GB
-    cache_hits: int = 0
-    cache_misses: int = 0
-    cache_high_watermark: int = 0
+    invocation_type: str
+
+    start_time: float  # Seconds since the epoch.
+    end_time: float  # Seconds since the epoch.
+
+    start_ram_gb: float  # GB
+    end_ram_gb: float  # GB
+
+    peak_vram_gb: float  # GB
+
+    def total_time(self) -> float:
+        return self.end_time - self.start_time
 
 
-@dataclass
-class NodeLog:
-    """Class for tracking node usage"""
+class GraphExecutionStats:
+    """Class for tracking execution stats of a graph."""
 
-    # {node_type => NodeStats}
-    nodes: Dict[str, NodeStats] = field(default_factory=dict)
+    def __init__(self):
+        self._node_stats_list: list[NodeExecutionStats] = []
+
+    def add_node_execution_stats(self, node_stats: NodeExecutionStats):
+        self._node_stats_list.append(node_stats)
+
+    def get_total_run_time(self) -> float:
+        """Get the total time spent executing nodes in the graph."""
+        total = 0.0
+        for node_stats in self._node_stats_list:
+            total += node_stats.total_time()
+        return total
+
+    def get_first_node_stats(self) -> NodeExecutionStats | None:
+        """Get the stats of the first node in the graph (by start_time)."""
+        first_node = None
+        for node_stats in self._node_stats_list:
+            if first_node is None or node_stats.start_time < first_node.start_time:
+                first_node = node_stats
+
+        assert first_node is not None
+        return first_node
+
+    def get_last_node_stats(self) -> NodeExecutionStats | None:
+        """Get the stats of the last node in the graph (by end_time)."""
+        last_node = None
+        for node_stats in self._node_stats_list:
+            if last_node is None or node_stats.end_time > last_node.end_time:
+                last_node = node_stats
+
+        return last_node
+
+    def get_pretty_log(self, graph_execution_state_id: str) -> str:
+        log = f"Graph stats: {graph_execution_state_id}\n"
+        log += f"{'Node':>30} {'Calls':>7}{'Seconds':>9} {'VRAM Used':>10}\n"
+
+        # Log stats aggregated by node type.
+        node_stats_by_type: dict[str, list[NodeExecutionStats]] = defaultdict(list)
+        for node_stats in self._node_stats_list:
+            node_stats_by_type[node_stats.invocation_type].append(node_stats)
+
+        for node_type, node_type_stats_list in node_stats_by_type.items():
+            num_calls = len(node_type_stats_list)
+            time_used = sum([n.total_time() for n in node_type_stats_list])
+            peak_vram = max([n.peak_vram_gb for n in node_type_stats_list])
+            log += f"{node_type:>30}  {num_calls:>4}   {time_used:7.3f}s     {peak_vram:4.3f}G\n"
+
+        # Log stats for the entire graph.
+        log += f"TOTAL GRAPH EXECUTION TIME: {self.get_total_run_time():7.3f}s\n"
+
+        first_node = self.get_first_node_stats()
+        last_node = self.get_last_node_stats()
+        if first_node is not None and last_node is not None:
+            total_wall_time = last_node.end_time - first_node.start_time
+            ram_change = last_node.end_ram_gb - first_node.start_ram_gb
+            log += f"TOTAL GRAPH WALL TIME: {total_wall_time:7.3f}s\n"
+            log += f"RAM used by InvokeAI process: {last_node.end_ram_gb:4.2f}G ({ram_change:+5.3f}G)\n"
+
+        return log

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -1,5 +1,5 @@
 import time
-from typing import Dict
+from contextlib import contextmanager
 
 import psutil
 import torch
@@ -7,161 +7,119 @@ import torch
 import invokeai.backend.util.logging as logger
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.backend.model_management.model_cache import CacheStats
 
 from .invocation_stats_base import InvocationStatsServiceBase
-from .invocation_stats_common import GIG, NodeLog, NodeStats
+from .invocation_stats_common import GraphExecutionStats, NodeExecutionStats
+
+# Size of 1GB in bytes.
+GB = 2**30
 
 
 class InvocationStatsService(InvocationStatsServiceBase):
     """Accumulate performance information about a running graph. Collects time spent in each node,
     as well as the maximum and current VRAM utilisation for CUDA systems"""
 
-    _invoker: Invoker
-
     def __init__(self):
-        # {graph_id => NodeLog}
-        self._stats: Dict[str, NodeLog] = {}
-        self._cache_stats: Dict[str, CacheStats] = {}
-        self.ram_used: float = 0.0
-        self.ram_changed: float = 0.0
+        # Maps graph_execution_state_id to GraphExecutionStats.
+        self._stats: dict[str, GraphExecutionStats] = {}
+        # Maps graph_execution_state_id to model manager CacheStats.
+        self._cache_stats: dict[str, CacheStats] = {}
 
     def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
 
-    class StatsContext:
-        """Context manager for collecting statistics."""
-
-        invocation: BaseInvocation
-        collector: "InvocationStatsServiceBase"
-        graph_id: str
-        start_time: float
-        ram_used: int
-        model_manager: ModelManagerServiceBase
-
-        def __init__(
-            self,
-            invocation: BaseInvocation,
-            graph_id: str,
-            model_manager: ModelManagerServiceBase,
-            collector: "InvocationStatsServiceBase",
-        ):
-            """Initialize statistics for this run."""
-            self.invocation = invocation
-            self.collector = collector
-            self.graph_id = graph_id
-            self.start_time = 0.0
-            self.ram_used = 0
-            self.model_manager = model_manager
-
-        def __enter__(self):
-            self.start_time = time.time()
-            if torch.cuda.is_available():
-                torch.cuda.reset_peak_memory_stats()
-            self.ram_used = psutil.Process().memory_info().rss
-            if self.model_manager:
-                self.model_manager.collect_cache_stats(self.collector._cache_stats[self.graph_id])
-
-        def __exit__(self, *args):
-            """Called on exit from the context."""
-            ram_used = psutil.Process().memory_info().rss
-            self.collector.update_mem_stats(
-                ram_used=ram_used / GIG,
-                ram_changed=(ram_used - self.ram_used) / GIG,
-            )
-            self.collector.update_invocation_stats(
-                graph_id=self.graph_id,
-                invocation_type=self.invocation.type,  # type: ignore # `type` is not on the `BaseInvocation` model, but *is* on all invocations
-                time_used=time.time() - self.start_time,
-                vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0,
-            )
-
-    def collect_stats(
-        self,
-        invocation: BaseInvocation,
-        graph_execution_state_id: str,
-    ) -> StatsContext:
-        if not self._stats.get(graph_execution_state_id):  # first time we're seeing this
-            self._stats[graph_execution_state_id] = NodeLog()
+    @contextmanager
+    def collect_stats(self, invocation: BaseInvocation, graph_execution_state_id: str):
+        if not self._stats.get(graph_execution_state_id):
+            # First time we're seeing this graph_execution_state_id.
+            self._stats[graph_execution_state_id] = GraphExecutionStats()
             self._cache_stats[graph_execution_state_id] = CacheStats()
-        return self.StatsContext(invocation, graph_execution_state_id, self._invoker.services.model_manager, self)
 
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        self._stats = {}
+            # Prune stale stats. There should be none since we're starting a new graph, but just in case.
+            self._prune_stale_stats()
+
+        # Record state before the invocation.
+        start_time = time.time()
+        start_ram = psutil.Process().memory_info().rss
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats()
+        if self._invoker.services.model_manager:
+            self._invoker.services.model_manager.collect_cache_stats(self._cache_stats[graph_execution_state_id])
 
-    def reset_stats(self, graph_execution_id: str):
         try:
-            self._stats.pop(graph_execution_id)
-        except KeyError:
-            logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_id}")
+            # Let the invocation run.
+            yield None
+        finally:
+            # Record state after the invocation.
+            node_stats = NodeExecutionStats(
+                invocation_type=invocation.type,
+                start_time=start_time,
+                end_time=time.time(),
+                start_ram_gb=start_ram / GB,
+                end_ram_gb=psutil.Process().memory_info().rss / GB,
+                peak_vram_gb=torch.cuda.max_memory_allocated() / GB if torch.cuda.is_available() else 0.0,
+            )
+            self._stats[graph_execution_state_id].add_node_execution_stats(node_stats)
 
-    def update_mem_stats(
-        self,
-        ram_used: float,
-        ram_changed: float,
-    ):
-        self.ram_used = ram_used
-        self.ram_changed = ram_changed
+    def _prune_stale_stats(self):
+        """Check all graphs being tracked and prune any that have completed/errored.
 
-    def update_invocation_stats(
-        self,
-        graph_id: str,
-        invocation_type: str,
-        time_used: float,
-        vram_used: float,
-    ):
-        if not self._stats[graph_id].nodes.get(invocation_type):
-            self._stats[graph_id].nodes[invocation_type] = NodeStats()
-        stats = self._stats[graph_id].nodes[invocation_type]
-        stats.calls += 1
-        stats.time_used += time_used
-        stats.max_vram = max(stats.max_vram, vram_used)
-
-    def log_stats(self):
-        completed = set()
-        errored = set()
-        for graph_id, _node_log in self._stats.items():
+        This shouldn't be necessary, but we don't have totally robust upstream handling of graph completions/errors, so
+        for now we call this function periodically to prevent them from accumulating.
+        """
+        to_prune = []
+        for graph_execution_state_id in self._stats:
             try:
-                current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
+                graph_execution_state = self._invoker.services.graph_execution_manager.get(graph_execution_state_id)
             except Exception:
-                errored.add(graph_id)
+                # TODO(ryand): What would cause this? Should this exception just be allowed to propagate?
+                logger.warning(f"Failed to get graph state for {graph_execution_state_id}.")
                 continue
 
-            if not current_graph_state.is_complete():
+            if not graph_execution_state.is_complete():
+                # The graph is still running, don't prune it.
                 continue
 
-            total_time = 0
-            logger.info(f"Graph stats: {graph_id}")
-            logger.info(f"{'Node':>30} {'Calls':>7}{'Seconds':>9} {'VRAM Used':>10}")
-            for node_type, stats in self._stats[graph_id].nodes.items():
-                logger.info(f"{node_type:>30}  {stats.calls:>4}   {stats.time_used:7.3f}s     {stats.max_vram:4.3f}G")
-                total_time += stats.time_used
+            to_prune.append(graph_execution_state_id)
 
-            cache_stats = self._cache_stats[graph_id]
-            hwm = cache_stats.high_watermark / GIG
-            tot = cache_stats.cache_size / GIG
-            loaded = sum(list(cache_stats.loaded_model_sizes.values())) / GIG
+        for graph_execution_state_id in to_prune:
+            del self._stats[graph_execution_state_id]
+            del self._cache_stats[graph_execution_state_id]
 
-            logger.info(f"TOTAL GRAPH EXECUTION TIME:  {total_time:7.3f}s")
-            logger.info("RAM used by InvokeAI process: " + "%4.2fG" % self.ram_used + f" ({self.ram_changed:+5.3f}G)")
-            logger.info(f"RAM used to load models: {loaded:4.2f}G")
-            if torch.cuda.is_available():
-                logger.info("VRAM in use: " + "%4.3fG" % (torch.cuda.memory_allocated() / GIG))
-            logger.info("RAM cache statistics:")
-            logger.info(f"   Model cache hits: {cache_stats.hits}")
-            logger.info(f"   Model cache misses: {cache_stats.misses}")
-            logger.info(f"   Models cached: {cache_stats.in_cache}")
-            logger.info(f"   Models cleared from cache: {cache_stats.cleared}")
-            logger.info(f"   Cache high water mark: {hwm:4.2f}/{tot:4.2f}G")
+        if len(to_prune) > 0:
+            logger.info(f"Pruned stale graph stats for {to_prune}.")
 
-            completed.add(graph_id)
+    def reset_stats(self, graph_execution_state_id: str):
+        try:
+            del self._stats[graph_execution_state_id]
+            del self._cache_stats[graph_execution_state_id]
+        except KeyError as e:
+            logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}.")
 
-        for graph_id in completed:
-            del self._stats[graph_id]
-            del self._cache_stats[graph_id]
+    def log_stats(self, graph_execution_state_id: str):
+        try:
+            graph_stats = self._stats[graph_execution_state_id]
+            cache_stats = self._cache_stats[graph_execution_state_id]
+        except KeyError as e:
+            logger.warning(f"Attempted to log statistics for unknown graph {graph_execution_state_id}: {e}.")
+            return
 
-        for graph_id in errored:
-            del self._stats[graph_id]
-            del self._cache_stats[graph_id]
+        log = graph_stats.get_pretty_log(graph_execution_state_id)
+
+        hwm = cache_stats.high_watermark / GB
+        tot = cache_stats.cache_size / GB
+        loaded = sum(list(cache_stats.loaded_model_sizes.values())) / GB
+        log += f"RAM used to load models: {loaded:4.2f}G\n"
+        if torch.cuda.is_available():
+            log += f"VRAM in use: {(torch.cuda.memory_allocated() / GB):4.3f}G\n"
+        log += "RAM cache statistics:\n"
+        log += f"   Model cache hits: {cache_stats.hits}\n"
+        log += f"   Model cache misses: {cache_stats.misses}\n"
+        log += f"   Models cached: {cache_stats.in_cache}\n"
+        log += f"   Models cleared from cache: {cache_stats.cleared}\n"
+        log += f"   Cache high water mark: {hwm:4.2f}/{tot:4.2f}G\n"
+        logger.info(log)
+
+        del self._stats[graph_execution_state_id]
+        del self._cache_stats[graph_execution_state_id]

invokeai/app/services/model_install/__init__.py

@@ -1,6 +1,7 @@
 """Initialization file for model install service package."""
 
 from .model_install_base import (
+    CivitaiModelSource,
     HFModelSource,
     InstallStatus,
     LocalModelSource,
@@ -22,4 +23,5 @@ __all__ = [
     "LocalModelSource",
     "HFModelSource",
     "URLModelSource",
+    "CivitaiModelSource",
 ]

invokeai/app/services/model_install/model_install_base.py

@@ -1,27 +1,42 @@
+# Copyright 2023 Lincoln D. Stein and the InvokeAI development team
+"""Baseclass definitions for the model installer."""
+
 import re
 import traceback
 from abc import ABC, abstractmethod
 from enum import Enum
 from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Set, Union
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, PrivateAttr, field_validator
 from pydantic.networks import AnyHttpUrl
 from typing_extensions import Annotated
 
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
 from invokeai.app.services.events import EventServiceBase
+from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_records import ModelRecordServiceBase
-from invokeai.backend.model_manager import AnyModelConfig
+from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
 
 
 class InstallStatus(str, Enum):
     """State of an install job running in the background."""
 
     WAITING = "waiting"  # waiting to be dequeued
+    DOWNLOADING = "downloading"  # downloading of model files in process
     RUNNING = "running"  # being processed
     COMPLETED = "completed"  # finished running
     ERROR = "error"  # terminated with an error message
+    CANCELLED = "cancelled"  # terminated with an error message
+
+
+class ModelInstallPart(BaseModel):
+    url: AnyHttpUrl
+    path: Path
+    bytes: int = 0
+    total_bytes: int = 0
 
 
 class UnknownInstallJobException(Exception):
@@ -74,12 +89,31 @@ class LocalModelSource(StringLikeSource):
         return Path(self.path).as_posix()
 
 
+class CivitaiModelSource(StringLikeSource):
+    """A Civitai version id, with optional variant and access token."""
+
+    version_id: int
+    variant: Optional[ModelRepoVariant] = None
+    access_token: Optional[str] = None
+    type: Literal["civitai"] = "civitai"
+
+    def __str__(self) -> str:
+        """Return string version of repoid when string rep needed."""
+        base: str = str(self.version_id)
+        base += f" ({self.variant})" if self.variant else ""
+        return base
+
+
 class HFModelSource(StringLikeSource):
-    """A HuggingFace repo_id, with optional variant and sub-folder."""
+    """
+    A HuggingFace repo_id with optional variant, sub-folder and access token.
+    Note that the variant option, if not provided to the constructor, will default to fp16, which is
+    what people (almost) always want.
+    """
 
     repo_id: str
-    variant: Optional[str] = None
-    subfolder: Optional[str | Path] = None
+    variant: Optional[ModelRepoVariant] = ModelRepoVariant.FP16
+    subfolder: Optional[Path] = None
     access_token: Optional[str] = None
     type: Literal["hf"] = "hf"
 
@@ -103,19 +137,22 @@ class URLModelSource(StringLikeSource):
 
     url: AnyHttpUrl
     access_token: Optional[str] = None
-    type: Literal["generic_url"] = "generic_url"
+    type: Literal["url"] = "url"
 
     def __str__(self) -> str:
         """Return string version of the url when string rep needed."""
         return str(self.url)
 
 
-ModelSource = Annotated[Union[LocalModelSource, HFModelSource, URLModelSource], Field(discriminator="type")]
+ModelSource = Annotated[
+    Union[LocalModelSource, HFModelSource, CivitaiModelSource, URLModelSource], Field(discriminator="type")
+]
 
 
 class ModelInstallJob(BaseModel):
     """Object that tracks the current status of an install request."""
 
+    id: int = Field(description="Unique ID for this job")
     status: InstallStatus = Field(default=InstallStatus.WAITING, description="Current status of install process")
     config_in: Dict[str, Any] = Field(
         default_factory=dict, description="Configuration information (e.g. 'description') to apply to model."
@@ -128,15 +165,74 @@ class ModelInstallJob(BaseModel):
     )
     source: ModelSource = Field(description="Source (URL, repo_id, or local path) of model")
     local_path: Path = Field(description="Path to locally-downloaded model; may be the same as the source")
-    error_type: Optional[str] = Field(default=None, description="Class name of the exception that led to status==ERROR")
-    error: Optional[str] = Field(default=None, description="Error traceback")  # noqa #501
+    bytes: Optional[int] = Field(
+        default=None, description="For a remote model, the number of bytes downloaded so far (may not be available)"
+    )
+    total_bytes: int = Field(default=0, description="Total size of the model to be installed")
+    source_metadata: Optional[AnyModelRepoMetadata] = Field(
+        default=None, description="Metadata provided by the model source"
+    )
+    download_parts: Set[DownloadJob] = Field(
+        default_factory=set, description="Download jobs contributing to this install"
+    )
+    # internal flags and transitory settings
+    _install_tmpdir: Optional[Path] = PrivateAttr(default=None)
+    _exception: Optional[Exception] = PrivateAttr(default=None)
 
     def set_error(self, e: Exception) -> None:
         """Record the error and traceback from an exception."""
-        self.error_type = e.__class__.__name__
-        self.error = "".join(traceback.format_exception(e))
+        self._exception = e
         self.status = InstallStatus.ERROR
 
+    def cancel(self) -> None:
+        """Call to cancel the job."""
+        self.status = InstallStatus.CANCELLED
+
+    @property
+    def error_type(self) -> Optional[str]:
+        """Class name of the exception that led to status==ERROR."""
+        return self._exception.__class__.__name__ if self._exception else None
+
+    @property
+    def error(self) -> Optional[str]:
+        """Error traceback."""
+        return "".join(traceback.format_exception(self._exception)) if self._exception else None
+
+    @property
+    def cancelled(self) -> bool:
+        """Set status to CANCELLED."""
+        return self.status == InstallStatus.CANCELLED
+
+    @property
+    def errored(self) -> bool:
+        """Return true if job has errored."""
+        return self.status == InstallStatus.ERROR
+
+    @property
+    def waiting(self) -> bool:
+        """Return true if job is waiting to run."""
+        return self.status == InstallStatus.WAITING
+
+    @property
+    def downloading(self) -> bool:
+        """Return true if job is downloading."""
+        return self.status == InstallStatus.DOWNLOADING
+
+    @property
+    def running(self) -> bool:
+        """Return true if job is running."""
+        return self.status == InstallStatus.RUNNING
+
+    @property
+    def complete(self) -> bool:
+        """Return true if job completed without errors."""
+        return self.status == InstallStatus.COMPLETED
+
+    @property
+    def in_terminal_state(self) -> bool:
+        """Return true if job is in a terminal state."""
+        return self.status in [InstallStatus.COMPLETED, InstallStatus.ERROR, InstallStatus.CANCELLED]
+
 
 class ModelInstallServiceBase(ABC):
     """Abstract base class for InvokeAI model installation."""
@@ -146,6 +242,8 @@ class ModelInstallServiceBase(ABC):
         self,
         app_config: InvokeAIAppConfig,
         record_store: ModelRecordServiceBase,
+        download_queue: DownloadQueueServiceBase,
+        metadata_store: ModelMetadataStore,
         event_bus: Optional["EventServiceBase"] = None,
     ):
         """
@@ -156,12 +254,14 @@ class ModelInstallServiceBase(ABC):
         :param event_bus: InvokeAI event bus for reporting events to.
         """
 
+    # make the invoker optional here because we don't need it and it
+    # makes the installer harder to use outside the web app
     @abstractmethod
-    def start(self, *args: Any, **kwarg: Any) -> None:
+    def start(self, invoker: Optional[Invoker] = None) -> None:
         """Start the installer service."""
 
     @abstractmethod
-    def stop(self, *args: Any, **kwarg: Any) -> None:
+    def stop(self, invoker: Optional[Invoker] = None) -> None:
         """Stop the model install service. After this the objection can be safely deleted."""
 
     @property
@@ -264,9 +364,13 @@ class ModelInstallServiceBase(ABC):
         """
 
     @abstractmethod
-    def get_job(self, source: ModelSource) -> List[ModelInstallJob]:
+    def get_job_by_source(self, source: ModelSource) -> List[ModelInstallJob]:
         """Return the ModelInstallJob(s) corresponding to the provided source."""
 
+    @abstractmethod
+    def get_job_by_id(self, id: int) -> ModelInstallJob:
+        """Return the ModelInstallJob corresponding to the provided id. Raises ValueError if no job has that ID."""
+
     @abstractmethod
     def list_jobs(self) -> List[ModelInstallJob]:  # noqa D102
         """
@@ -278,16 +382,19 @@ class ModelInstallServiceBase(ABC):
         """Prune all completed and errored jobs."""
 
     @abstractmethod
-    def wait_for_installs(self) -> List[ModelInstallJob]:
+    def cancel_job(self, job: ModelInstallJob) -> None:
+        """Cancel the indicated job."""
+
+    @abstractmethod
+    def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:
         """
         Wait for all pending installs to complete.
 
         This will block until all pending installs have
-        completed, been cancelled, or errored out. It will
-        block indefinitely if one or more jobs are in the
-        paused state.
+        completed, been cancelled, or errored out.
 
-        It will return the current list of jobs.
+        :param timeout: Wait up to indicated number of seconds. Raise an Exception('timeout') if
+        installs do not complete within the indicated time.
         """
 
     @abstractmethod

invokeai/app/services/model_install/model_install_default.py

@@ -1,60 +1,72 @@
 """Model installation class."""
 
+import os
+import re
 import threading
+import time
 from hashlib import sha256
-from logging import Logger
 from pathlib import Path
-from queue import Queue
+from queue import Empty, Queue
 from random import randbytes
 from shutil import copyfile, copytree, move, rmtree
+from tempfile import mkdtemp
 from typing import Any, Dict, List, Optional, Set, Union
 
+from huggingface_hub import HfFolder
+from pydantic.networks import AnyHttpUrl
+from requests import Session
+
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.events import EventServiceBase
-from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase, UnknownModelException
+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, ModelRecordServiceSQL
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
     InvalidModelConfigException,
+    ModelRepoVariant,
     ModelType,
 )
 from invokeai.backend.model_manager.hash import FastModelHash
+from invokeai.backend.model_manager.metadata import (
+    AnyModelRepoMetadata,
+    CivitaiMetadataFetch,
+    HuggingFaceMetadataFetch,
+    ModelMetadataStore,
+    ModelMetadataWithFiles,
+    RemoteModelFile,
+)
 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 (
+    CivitaiModelSource,
+    HFModelSource,
     InstallStatus,
     LocalModelSource,
     ModelInstallJob,
     ModelInstallServiceBase,
     ModelSource,
+    URLModelSource,
 )
 
-# marker that the queue is done and that thread should exit
-STOP_JOB = ModelInstallJob(
-    source=LocalModelSource(path="stop"),
-    local_path=Path("/dev/null"),
-)
+TMPDIR_PREFIX = "tmpinstall_"
 
 
 class ModelInstallService(ModelInstallServiceBase):
     """class for InvokeAI model installation."""
 
-    _app_config: InvokeAIAppConfig
-    _record_store: ModelRecordServiceBase
-    _event_bus: Optional[EventServiceBase] = None
-    _install_queue: Queue[ModelInstallJob]
-    _install_jobs: List[ModelInstallJob]
-    _logger: Logger
-    _cached_model_paths: Set[Path]
-    _models_installed: Set[str]
-
     def __init__(
         self,
         app_config: InvokeAIAppConfig,
         record_store: ModelRecordServiceBase,
+        download_queue: DownloadQueueServiceBase,
+        metadata_store: Optional[ModelMetadataStore] = None,
         event_bus: Optional[EventServiceBase] = None,
+        session: Optional[Session] = None,
     ):
         """
         Initialize the installer object.
@@ -67,10 +79,26 @@ class ModelInstallService(ModelInstallServiceBase):
         self._record_store = record_store
         self._event_bus = event_bus
         self._logger = InvokeAILogger.get_logger(name=self.__class__.__name__)
-        self._install_jobs = []
-        self._install_queue = Queue()
-        self._cached_model_paths = set()
-        self._models_installed = set()
+        self._install_jobs: List[ModelInstallJob] = []
+        self._install_queue: Queue[ModelInstallJob] = Queue()
+        self._cached_model_paths: Set[Path] = set()
+        self._models_installed: Set[str] = set()
+        self._lock = threading.Lock()
+        self._stop_event = threading.Event()
+        self._downloads_changed_event = threading.Event()
+        self._download_queue = download_queue
+        self._download_cache: Dict[AnyHttpUrl, ModelInstallJob] = {}
+        self._running = False
+        self._session = session
+        self._next_job_id = 0
+        # There may not necessarily be a metadata store initialized
+        # so we create one and initialize it with the same sql database
+        # used by the record store service.
+        if metadata_store:
+            self._metadata_store = metadata_store
+        else:
+            assert isinstance(record_store, ModelRecordServiceSQL)
+            self._metadata_store = ModelMetadataStore(record_store.db)
 
     @property
     def app_config(self) -> InvokeAIAppConfig:  # noqa D102
@@ -84,69 +112,31 @@ class ModelInstallService(ModelInstallServiceBase):
     def event_bus(self) -> Optional[EventServiceBase]:  # noqa D102
         return self._event_bus
 
-    def start(self, *args: Any, **kwarg: Any) -> None:
+    # make the invoker optional here because we don't need it and it
+    # makes the installer harder to use outside the web app
+    def start(self, invoker: Optional[Invoker] = None) -> None:
         """Start the installer thread."""
-        self._start_installer_thread()
-        self.sync_to_config()
+        with self._lock:
+            if self._running:
+                raise Exception("Attempt to start the installer service twice")
+            self._start_installer_thread()
+            self._remove_dangling_install_dirs()
+            self.sync_to_config()
 
-    def stop(self, *args: Any, **kwarg: Any) -> None:
+    def stop(self, invoker: Optional[Invoker] = None) -> None:
         """Stop the installer thread; after this the object can be deleted and garbage collected."""
-        self._install_queue.put(STOP_JOB)
-
-    def _start_installer_thread(self) -> None:
-        threading.Thread(target=self._install_next_item, daemon=True).start()
-
-    def _install_next_item(self) -> None:
-        done = False
-        while not done:
-            job = self._install_queue.get()
-            if job == STOP_JOB:
-                done = True
-                continue
-
-            assert job.local_path is not None
-            try:
-                self._signal_job_running(job)
-                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)
-                self._signal_job_completed(job)
-
-            except (OSError, DuplicateModelException, InvalidModelConfigException) as excp:
-                self._signal_job_errored(job, excp)
-            finally:
-                self._install_queue.task_done()
-        self._logger.info("Install thread exiting")
-
-    def _signal_job_running(self, job: ModelInstallJob) -> None:
-        job.status = InstallStatus.RUNNING
-        self._logger.info(f"{job.source}: model installation started")
-        if self._event_bus:
-            self._event_bus.emit_model_install_started(str(job.source))
-
-    def _signal_job_completed(self, job: ModelInstallJob) -> None:
-        job.status = InstallStatus.COMPLETED
-        assert job.config_out
-        self._logger.info(
-            f"{job.source}: model installation completed. {job.local_path} registered key {job.config_out.key}"
-        )
-        if self._event_bus:
-            assert job.local_path is not None
-            assert job.config_out is not None
-            key = job.config_out.key
-            self._event_bus.emit_model_install_completed(str(job.source), key)
-
-    def _signal_job_errored(self, job: ModelInstallJob, excp: Exception) -> None:
-        job.set_error(excp)
-        self._logger.info(f"{job.source}: model installation encountered an exception: {job.error_type}")
-        if self._event_bus:
-            error_type = job.error_type
-            error = job.error
-            assert error_type is not None
-            assert error is not None
-            self._event_bus.emit_model_install_error(str(job.source), error_type, error)
+        with self._lock:
+            if not self._running:
+                raise Exception("Attempt to stop the install service before it was started")
+            self._stop_event.set()
+            with self._install_queue.mutex:
+                self._install_queue.queue.clear()  # get rid of pending jobs
+            active_jobs = [x for x in self.list_jobs() if x.running]
+            if active_jobs:
+                self._logger.warning("Waiting for active install job to complete")
+            self.wait_for_installs()
+            self._download_cache.clear()
+            self._running = False
 
     def register_path(
         self,
@@ -172,7 +162,12 @@ class ModelInstallService(ModelInstallServiceBase):
         info: AnyModelConfig = self._probe_model(Path(model_path), config)
         old_hash = info.original_hash
         dest_path = self.app_config.models_path / info.base.value / info.type.value / model_path.name
-        new_path = self._copy_model(model_path, dest_path)
+        try:
+            new_path = self._copy_model(model_path, dest_path)
+        except FileExistsError as excp:
+            raise DuplicateModelException(
+                f"A model named {model_path.name} is already installed at {dest_path.as_posix()}"
+            ) from excp
         new_hash = FastModelHash.hash(new_path)
         assert new_hash == old_hash, f"{model_path}: Model hash changed during installation, possibly corrupted."
 
@@ -182,43 +177,56 @@ class ModelInstallService(ModelInstallServiceBase):
             info,
         )
 
-    def import_model(
-        self,
-        source: ModelSource,
-        config: Optional[Dict[str, Any]] = None,
-    ) -> ModelInstallJob:  # noqa D102
-        if not config:
-            config = {}
+    def import_model(self, source: ModelSource, config: Optional[Dict[str, Any]] = None) -> ModelInstallJob:  # noqa D102
+        if isinstance(source, LocalModelSource):
+            install_job = self._import_local_model(source, config)
+            self._install_queue.put(install_job)  # synchronously install
+        elif isinstance(source, CivitaiModelSource):
+            install_job = self._import_from_civitai(source, config)
+        elif isinstance(source, HFModelSource):
+            install_job = self._import_from_hf(source, config)
+        elif isinstance(source, URLModelSource):
+            install_job = self._import_from_url(source, config)
+        else:
+            raise ValueError(f"Unsupported model source: '{type(source)}'")
 
-        # Installing a local path
-        if isinstance(source, LocalModelSource) and Path(source.path).exists():  # a path that is already on disk
-            job = ModelInstallJob(
-                source=source,
-                config_in=config,
-                local_path=Path(source.path),
-            )
-            self._install_jobs.append(job)
-            self._install_queue.put(job)
-            return job
-
-        else:  # here is where we'd download a URL or repo_id. Implementation pending download queue.
-            raise UnknownModelException("File or directory not found")
+        self._install_jobs.append(install_job)
+        return install_job
 
     def list_jobs(self) -> List[ModelInstallJob]:  # noqa D102
         return self._install_jobs
 
-    def get_job(self, source: ModelSource) -> List[ModelInstallJob]:  # noqa D102
+    def get_job_by_source(self, source: ModelSource) -> List[ModelInstallJob]:  # noqa D102
         return [x for x in self._install_jobs if x.source == source]
 
-    def wait_for_installs(self) -> List[ModelInstallJob]:  # noqa D102
+    def get_job_by_id(self, id: int) -> ModelInstallJob:  # noqa D102
+        jobs = [x for x in self._install_jobs if x.id == id]
+        if not jobs:
+            raise ValueError(f"No job with id {id} known")
+        assert len(jobs) == 1
+        assert isinstance(jobs[0], ModelInstallJob)
+        return jobs[0]
+
+    def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:  # noqa D102
+        """Block until all installation jobs are done."""
+        start = time.time()
+        while len(self._download_cache) > 0:
+            if self._downloads_changed_event.wait(timeout=5):  # in case we miss an event
+                self._downloads_changed_event.clear()
+            if timeout > 0 and time.time() - start > timeout:
+                raise Exception("Timeout exceeded")
         self._install_queue.join()
         return self._install_jobs
 
+    def cancel_job(self, job: ModelInstallJob) -> None:
+        """Cancel the indicated job."""
+        job.cancel()
+        with self._lock:
+            self._cancel_download_parts(job)
+
     def prune_jobs(self) -> None:
         """Prune all completed and errored jobs."""
-        unfinished_jobs = [
-            x for x in self._install_jobs if x.status not in [InstallStatus.COMPLETED, InstallStatus.ERROR]
-        ]
+        unfinished_jobs = [x for x in self._install_jobs if not x.in_terminal_state]
         self._install_jobs = unfinished_jobs
 
     def sync_to_config(self) -> None:
@@ -234,10 +242,108 @@ class ModelInstallService(ModelInstallServiceBase):
         self._cached_model_paths = {Path(x.path) for x in self.record_store.all_models()}
         callback = self._scan_install if install else self._scan_register
         search = ModelSearch(on_model_found=callback)
-        self._models_installed: Set[str] = set()
+        self._models_installed.clear()
         search.search(scan_dir)
         return list(self._models_installed)
 
+    def unregister(self, key: str) -> None:  # noqa D102
+        self.record_store.del_model(key)
+
+    def delete(self, key: str) -> None:  # noqa D102
+        """Unregister the model. Delete its files only if they are within our models directory."""
+        model = self.record_store.get_model(key)
+        models_dir = self.app_config.models_path
+        model_path = models_dir / model.path
+        if model_path.is_relative_to(models_dir):
+            self.unconditionally_delete(key)
+        else:
+            self.unregister(key)
+
+    def unconditionally_delete(self, key: str) -> None:  # noqa D102
+        model = self.record_store.get_model(key)
+        path = self.app_config.models_path / model.path
+        if path.is_dir():
+            rmtree(path)
+        else:
+            path.unlink()
+        self.unregister(key)
+
+    # --------------------------------------------------------------------------------------------
+    # Internal functions that manage the installer threads
+    # --------------------------------------------------------------------------------------------
+    def _start_installer_thread(self) -> None:
+        threading.Thread(target=self._install_next_item, daemon=True).start()
+        self._running = True
+
+    def _install_next_item(self) -> None:
+        done = False
+        while not done:
+            if self._stop_event.is_set():
+                done = True
+                continue
+            try:
+                job = self._install_queue.get(timeout=1)
+            except Empty:
+                continue
+
+            assert job.local_path is not None
+            try:
+                if job.cancelled:
+                    self._signal_job_cancelled(job)
+
+                elif job.errored:
+                    self._signal_job_errored(job)
+
+                elif (
+                    job.waiting or job.downloading
+                ):  # local jobs will be in waiting state, remote jobs will be downloading state
+                    job.total_bytes = self._stat_size(job.local_path)
+                    job.bytes = job.total_bytes
+                    self._signal_job_running(job)
+                    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:
+                if any(x.content_type is not None and "text/html" in x.content_type for x in job.download_parts):
+                    job.set_error(
+                        InvalidModelConfigException(
+                            f"At least one file in {job.local_path} is an HTML page, not a model. This can happen when an access token is required to download."
+                        )
+                    )
+                else:
+                    job.set_error(excp)
+                self._signal_job_errored(job)
+
+            except (OSError, DuplicateModelException) as excp:
+                job.set_error(excp)
+                self._signal_job_errored(job)
+
+            finally:
+                # if this is an install of a remote file, then clean up the temporary directory
+                if job._install_tmpdir is not None:
+                    rmtree(job._install_tmpdir)
+                self._install_queue.task_done()
+
+        self._logger.info("Install thread exiting")
+
+    # --------------------------------------------------------------------------------------------
+    # Internal functions that manage the models directory
+    # --------------------------------------------------------------------------------------------
+    def _remove_dangling_install_dirs(self) -> None:
+        """Remove leftover tmpdirs from aborted installs."""
+        path = self._app_config.models_path
+        for tmpdir in path.glob(f"{TMPDIR_PREFIX}*"):
+            self._logger.info(f"Removing dangling temporary directory {tmpdir}")
+            rmtree(tmpdir)
+
     def _scan_models_directory(self) -> None:
         """
         Scan the models directory for new and missing models.
@@ -320,28 +426,6 @@ class ModelInstallService(ModelInstallServiceBase):
             pass
         return True
 
-    def unregister(self, key: str) -> None:  # noqa D102
-        self.record_store.del_model(key)
-
-    def delete(self, key: str) -> None:  # noqa D102
-        """Unregister the model. Delete its files only if they are within our models directory."""
-        model = self.record_store.get_model(key)
-        models_dir = self.app_config.models_path
-        model_path = models_dir / model.path
-        if model_path.is_relative_to(models_dir):
-            self.unconditionally_delete(key)
-        else:
-            self.unregister(key)
-
-    def unconditionally_delete(self, key: str) -> None:  # noqa D102
-        model = self.record_store.get_model(key)
-        path = self.app_config.models_path / model.path
-        if path.is_dir():
-            rmtree(path)
-        else:
-            path.unlink()
-        self.unregister(key)
-
     def _copy_model(self, old_path: Path, new_path: Path) -> Path:
         if old_path == new_path:
             return old_path
@@ -397,3 +481,279 @@ class ModelInstallService(ModelInstallServiceBase):
             info.config = legacy_conf.relative_to(self.app_config.root_dir).as_posix()
         self.record_store.add_model(key, info)
         return key
+
+    def _next_id(self) -> int:
+        with self._lock:
+            id = self._next_job_id
+            self._next_job_id += 1
+        return id
+
+    @staticmethod
+    def _guess_variant() -> ModelRepoVariant:
+        """Guess the best HuggingFace variant type to download."""
+        precision = choose_precision(choose_torch_device())
+        return ModelRepoVariant.FP16 if precision == "float16" else ModelRepoVariant.DEFAULT
+
+    def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+        return ModelInstallJob(
+            id=self._next_id(),
+            source=source,
+            config_in=config or {},
+            local_path=Path(source.path),
+            inplace=source.inplace,
+        )
+
+    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))
+        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)
+
+    def _import_from_hf(self, source: HFModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
+        # Add user's cached access token to HuggingFace requests
+        source.access_token = source.access_token or HfFolder.get_token()
+        if not source.access_token:
+            self._logger.info("No HuggingFace access token present; some models may not be downloadable.")
+
+        metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id)
+        assert isinstance(metadata, ModelMetadataWithFiles)
+        remote_files = metadata.download_urls(
+            variant=source.variant or self._guess_variant(),
+            subfolder=source.subfolder,
+            session=self._session,
+        )
+
+        return self._import_remote_model(
+            source=source,
+            config=config,
+            remote_files=remote_files,
+            metadata=metadata,
+        )
+
+    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
+        if metadata and isinstance(metadata, ModelMetadataWithFiles):
+            remote_files = metadata.download_urls(session=self._session)
+        else:
+            remote_files = [RemoteModelFile(url=source.url, path=Path("."), size=0)]
+
+        return self._import_remote_model(
+            source=source,
+            config=config,
+            metadata=metadata,
+            remote_files=remote_files,
+        )
+
+    def _import_remote_model(
+        self,
+        source: ModelSource,
+        remote_files: List[RemoteModelFile],
+        metadata: Optional[AnyModelRepoMetadata],
+        config: Optional[Dict[str, Any]],
+    ) -> ModelInstallJob:
+        # TODO: Replace with tempfile.tmpdir() when multithreading is cleaned up.
+        # Currently the tmpdir isn't automatically removed at exit because it is
+        # being held in a daemon thread.
+        tmpdir = Path(
+            mkdtemp(
+                dir=self._app_config.models_path,
+                prefix=TMPDIR_PREFIX,
+            )
+        )
+        install_job = ModelInstallJob(
+            id=self._next_id(),
+            source=source,
+            config_in=config or {},
+            source_metadata=metadata,
+            local_path=tmpdir,  # local path may change once the download has started due to content-disposition handling
+            bytes=0,
+            total_bytes=0,
+        )
+        # we remember the path up to the top of the tmpdir so that it may be
+        # removed safely at the end of the install process.
+        install_job._install_tmpdir = tmpdir
+        assert install_job.total_bytes is not None  # to avoid type checking complaints in the loop below
+
+        self._logger.info(f"Queuing {source} for downloading")
+        for model_file in remote_files:
+            url = model_file.url
+            path = model_file.path
+            self._logger.info(f"Downloading {url} => {path}")
+            install_job.total_bytes += model_file.size
+            assert hasattr(source, "access_token")
+            dest = tmpdir / path.parent
+            dest.mkdir(parents=True, exist_ok=True)
+            download_job = DownloadJob(
+                source=url,
+                dest=dest,
+                access_token=source.access_token,
+            )
+            self._download_cache[download_job.source] = install_job  # matches a download job to an install job
+            install_job.download_parts.add(download_job)
+
+            self._download_queue.submit_download_job(
+                download_job,
+                on_start=self._download_started_callback,
+                on_progress=self._download_progress_callback,
+                on_complete=self._download_complete_callback,
+                on_error=self._download_error_callback,
+                on_cancelled=self._download_cancelled_callback,
+            )
+        return install_job
+
+    def _stat_size(self, path: Path) -> int:
+        size = 0
+        if path.is_file():
+            size = path.stat().st_size
+        elif path.is_dir():
+            for root, _, files in os.walk(path):
+                size += sum(self._stat_size(Path(root, x)) for x in files)
+        return size
+
+    # ------------------------------------------------------------------
+    # Callbacks are executed by the download queue in a separate thread
+    # ------------------------------------------------------------------
+    def _download_started_callback(self, download_job: DownloadJob) -> None:
+        self._logger.info(f"{download_job.source}: model download started")
+        with self._lock:
+            install_job = self._download_cache[download_job.source]
+            install_job.status = InstallStatus.DOWNLOADING
+
+            assert download_job.download_path
+            if install_job.local_path == install_job._install_tmpdir:
+                partial_path = download_job.download_path.relative_to(install_job._install_tmpdir)
+                dest_name = partial_path.parts[0]
+                install_job.local_path = install_job._install_tmpdir / dest_name
+
+            # Update the total bytes count for remote sources.
+            if not install_job.total_bytes:
+                install_job.total_bytes = sum(x.total_bytes for x in install_job.download_parts)
+
+    def _download_progress_callback(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            install_job = self._download_cache[download_job.source]
+            if install_job.cancelled:  # This catches the case in which the caller directly calls job.cancel()
+                self._cancel_download_parts(install_job)
+            else:
+                # update sizes
+                install_job.bytes = sum(x.bytes for x in install_job.download_parts)
+                self._signal_job_downloading(install_job)
+
+    def _download_complete_callback(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            install_job = self._download_cache[download_job.source]
+            self._download_cache.pop(download_job.source, None)
+
+            # are there any more active jobs left in this task?
+            if all(x.complete for x in install_job.download_parts):
+                #  now enqueue job for actual installation into the models directory
+                self._install_queue.put(install_job)
+
+            # Let other threads know that the number of downloads has changed
+            self._downloads_changed_event.set()
+
+    def _download_error_callback(self, download_job: DownloadJob, excp: Optional[Exception] = None) -> None:
+        with self._lock:
+            install_job = self._download_cache.pop(download_job.source, None)
+            assert install_job is not None
+            assert excp is not None
+            install_job.set_error(excp)
+            self._logger.error(
+                f"Cancelling {install_job.source} due to an error while downloading {download_job.source}: {str(excp)}"
+            )
+            self._cancel_download_parts(install_job)
+
+            # Let other threads know that the number of downloads has changed
+            self._downloads_changed_event.set()
+
+    def _download_cancelled_callback(self, download_job: DownloadJob) -> None:
+        with self._lock:
+            install_job = self._download_cache.pop(download_job.source, None)
+            if not install_job:
+                return
+            self._downloads_changed_event.set()
+            self._logger.warning(f"Download {download_job.source} cancelled.")
+            # if install job has already registered an error, then do not replace its status with cancelled
+            if not install_job.errored:
+                install_job.cancel()
+            self._cancel_download_parts(install_job)
+
+            # Let other threads know that the number of downloads has changed
+            self._downloads_changed_event.set()
+
+    def _cancel_download_parts(self, install_job: ModelInstallJob) -> None:
+        # on multipart downloads, _cancel_components() will get called repeatedly from the download callbacks
+        # do not lock here because it gets called within a locked context
+        for s in install_job.download_parts:
+            self._download_queue.cancel_job(s)
+
+        if all(x.in_terminal_state for x in install_job.download_parts):
+            # When all parts have reached their terminal state, we finalize the job to clean up the temporary directory and other resources
+            self._install_queue.put(install_job)
+
+    # ------------------------------------------------------------------------------------------------
+    # Internal methods that put events on the event bus
+    # ------------------------------------------------------------------------------------------------
+    def _signal_job_running(self, job: ModelInstallJob) -> None:
+        job.status = InstallStatus.RUNNING
+        self._logger.info(f"{job.source}: model installation started")
+        if self._event_bus:
+            self._event_bus.emit_model_install_running(str(job.source))
+
+    def _signal_job_downloading(self, job: ModelInstallJob) -> None:
+        if self._event_bus:
+            parts: List[Dict[str, str | int]] = [
+                {
+                    "url": str(x.source),
+                    "local_path": str(x.download_path),
+                    "bytes": x.bytes,
+                    "total_bytes": x.total_bytes,
+                }
+                for x in job.download_parts
+            ]
+            assert job.bytes is not None
+            assert job.total_bytes is not None
+            self._event_bus.emit_model_install_downloading(
+                str(job.source),
+                local_path=job.local_path.as_posix(),
+                parts=parts,
+                bytes=job.bytes,
+                total_bytes=job.total_bytes,
+            )
+
+    def _signal_job_completed(self, job: ModelInstallJob) -> None:
+        job.status = InstallStatus.COMPLETED
+        assert job.config_out
+        self._logger.info(
+            f"{job.source}: model installation completed. {job.local_path} registered key {job.config_out.key}"
+        )
+        if self._event_bus:
+            assert job.local_path is not None
+            assert job.config_out is not None
+            key = job.config_out.key
+            self._event_bus.emit_model_install_completed(str(job.source), key)
+
+    def _signal_job_errored(self, job: ModelInstallJob) -> None:
+        self._logger.info(f"{job.source}: model installation encountered an exception: {job.error_type}\n{job.error}")
+        if self._event_bus:
+            error_type = job.error_type
+            error = job.error
+            assert error_type is not None
+            assert error is not None
+            self._event_bus.emit_model_install_error(str(job.source), error_type, error)
+
+    def _signal_job_cancelled(self, job: ModelInstallJob) -> None:
+        self._logger.info(f"{job.source}: model installation was cancelled")
+        if self._event_bus:
+            self._event_bus.emit_model_install_cancelled(str(job.source))

invokeai/app/services/model_records/__init__.py

@@ -4,6 +4,8 @@ from .model_records_base import (  # noqa F401
     InvalidModelException,
     ModelRecordServiceBase,
     UnknownModelException,
+    ModelSummary,
+    ModelRecordOrderBy,
 )
 from .model_records_sql import ModelRecordServiceSQL  # noqa F401
 
@@ -13,4 +15,6 @@ __all__ = [
     "DuplicateModelException",
     "InvalidModelException",
     "UnknownModelException",
+    "ModelSummary",
+    "ModelRecordOrderBy",
 ]

invokeai/app/services/model_records/model_records_base.py

@@ -4,10 +4,15 @@ 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 List, Optional, Union
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
+from pydantic import BaseModel, Field
+
+from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
 
 
 class DuplicateModelException(Exception):
@@ -26,11 +31,33 @@ class ConfigFileVersionMismatchException(Exception):
     """Raised on an attempt to open a config with an incompatible version."""
 
 
+class ModelRecordOrderBy(str, Enum):
+    """The order in which to return model summaries."""
+
+    Default = "default"  # order by type, base, format and name
+    Type = "type"
+    Base = "base"
+    Name = "name"
+    Format = "format"
+
+
+class ModelSummary(BaseModel):
+    """A short summary of models for UI listing purposes."""
+
+    key: str = Field(description="model key")
+    type: ModelType = Field(description="model type")
+    base: BaseModelType = Field(description="base model")
+    format: ModelFormat = Field(description="model format")
+    name: str = Field(description="model name")
+    description: str = Field(description="short description of model")
+    tags: Set[str] = Field(description="tags associated with model")
+
+
 class ModelRecordServiceBase(ABC):
     """Abstract base class for storage and retrieval of model configs."""
 
     @abstractmethod
-    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
+    def add_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -54,7 +81,7 @@ class ModelRecordServiceBase(ABC):
         pass
 
     @abstractmethod
-    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
+    def update_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
         """
         Update the model, returning the updated version.
 
@@ -75,6 +102,47 @@ class ModelRecordServiceBase(ABC):
         """
         pass
 
+    @property
+    @abstractmethod
+    def metadata_store(self) -> ModelMetadataStore:
+        """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
+    ) -> PaginatedResults[ModelSummary]:
+        """Return a paginated summary listing of each model in the database."""
+        pass
+
     @abstractmethod
     def exists(self, key: str) -> bool:
         """

invokeai/app/services/model_records/model_records_sql.py

@@ -42,9 +42,11 @@ Typical usage:
 
 import json
 import sqlite3
+from math import ceil
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
+from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
@@ -52,11 +54,14 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore, UnknownMetadataException
 
 from ..shared.sqlite.sqlite_database import SqliteDatabase
 from .model_records_base import (
     DuplicateModelException,
+    ModelRecordOrderBy,
     ModelRecordServiceBase,
+    ModelSummary,
     UnknownModelException,
 )
 
@@ -64,9 +69,6 @@ from .model_records_base import (
 class ModelRecordServiceSQL(ModelRecordServiceBase):
     """Implementation of the ModelConfigStore ABC using a SQL database."""
 
-    _db: SqliteDatabase
-    _cursor: sqlite3.Cursor
-
     def __init__(self, db: SqliteDatabase):
         """
         Initialize a new object from preexisting sqlite3 connection and threading lock objects.
@@ -78,7 +80,12 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         self._db = db
         self._cursor = self._db.conn.cursor()
 
-    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
+    @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:
         """
         Add a model to the database.
 
@@ -293,3 +300,95 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             )
             results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
+
+    @property
+    def metadata_store(self) -> ModelMetadataStore:
+        """Return a ModelMetadataStore initialized on the same database."""
+        return ModelMetadataStore(self._db)
+
+    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 = ModelMetadataStore(self._db)
+        keys = store.search_by_tag(tags)
+        return [self.get_model(x) for x in keys]
+
+    def list_tags(self) -> Set[str]:
+        """Return a unique set of all the model tags in the metadata database."""
+        store = ModelMetadataStore(self._db)
+        return store.list_tags()
+
+    def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:
+        """List metadata for all models that have it."""
+        store = ModelMetadataStore(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."""
+        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",
+        }
+
+        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;
+                """,
+                (),
+            )
+            total = int(self._cursor.fetchone()[0])
+
+            # query2: fetch key fields from the join of model_config and model_metadata
+            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
+                ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason
+                LIMIT ?
+                OFFSET ?;
+                """,
+                (
+                    per_page,
+                    page * per_page,
+                ),
+            )
+            rows = self._cursor.fetchall()
+            items = [ModelSummary.model_validate(_fixup(dict(x))) for x in rows]
+            return PaginatedResults(
+                page=page, pages=ceil(total / per_page), per_page=per_page, total=total, items=items
+            )

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

@@ -6,6 +6,7 @@ from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_1 import build_migration_1
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_2 import build_migration_2
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_3 import build_migration_3
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_4 import build_migration_4
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -28,7 +29,8 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator = SqliteMigrator(db=db)
     migrator.register_migration(build_migration_1())
     migrator.register_migration(build_migration_2(image_files=image_files, logger=logger))
-    migrator.register_migration(build_migration_3())
+    migrator.register_migration(build_migration_3(app_config=config, logger=logger))
+    migrator.register_migration(build_migration_4())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_2.py

@@ -11,8 +11,6 @@ from invokeai.app.services.workflow_records.workflow_records_common import (
     UnsafeWorkflowWithVersionValidator,
 )
 
-from .util.migrate_yaml_config_1 import MigrateModelYamlToDb1
-
 
 class Migration2Callback:
     def __init__(self, image_files: ImageFileStorageBase, logger: Logger):
@@ -25,8 +23,6 @@ class Migration2Callback:
         self._drop_old_workflow_tables(cursor)
         self._add_workflow_library(cursor)
         self._drop_model_manager_metadata(cursor)
-        self._recreate_model_config(cursor)
-        self._migrate_model_config_records(cursor)
         self._migrate_embedded_workflows(cursor)
 
     def _add_images_has_workflow(self, cursor: sqlite3.Cursor) -> None:
@@ -100,45 +96,6 @@ class Migration2Callback:
         """Drops the `model_manager_metadata` table."""
         cursor.execute("DROP TABLE IF EXISTS model_manager_metadata;")
 
-    def _recreate_model_config(self, cursor: sqlite3.Cursor) -> None:
-        """
-        Drops the `model_config` table, recreating it.
-
-        In 3.4.0, this table used explicit columns but was changed to use json_extract 3.5.0.
-
-        Because this table is not used in production, we are able to simply drop it and recreate it.
-        """
-
-        cursor.execute("DROP TABLE IF EXISTS model_config;")
-
-        cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS model_config (
-                id TEXT NOT NULL PRIMARY KEY,
-                -- The next 3 fields are enums in python, unrestricted string here
-                base TEXT GENERATED ALWAYS as (json_extract(config, '$.base')) VIRTUAL NOT NULL,
-                type TEXT GENERATED ALWAYS as (json_extract(config, '$.type')) VIRTUAL NOT NULL,
-                name TEXT GENERATED ALWAYS as (json_extract(config, '$.name')) VIRTUAL NOT NULL,
-                path TEXT GENERATED ALWAYS as (json_extract(config, '$.path')) VIRTUAL NOT NULL,
-                format TEXT GENERATED ALWAYS as (json_extract(config, '$.format')) VIRTUAL NOT NULL,
-                original_hash TEXT, -- could be null
-                -- 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)
-            );
-            """
-        )
-
-    def _migrate_model_config_records(self, cursor: sqlite3.Cursor) -> None:
-        """After updating the model config table, we repopulate it."""
-        model_record_migrator = MigrateModelYamlToDb1(cursor)
-        model_record_migrator.migrate()
-
     def _migrate_embedded_workflows(self, cursor: sqlite3.Cursor) -> None:
         """
         In the v3.5.0 release, InvokeAI changed how it handles embedded workflows. The `images` table in

invokeai/app/services/shared/sqlite_migrator/migrations/migration_3.py

@@ -1,13 +1,16 @@
 import sqlite3
+from logging import Logger
 
+from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
 
 from .util.migrate_yaml_config_1 import MigrateModelYamlToDb1
 
 
 class Migration3Callback:
-    def __init__(self) -> None:
-        pass
+    def __init__(self, app_config: InvokeAIAppConfig, logger: Logger) -> None:
+        self._app_config = app_config
+        self._logger = logger
 
     def __call__(self, cursor: sqlite3.Cursor) -> None:
         self._drop_model_manager_metadata(cursor)
@@ -54,11 +57,12 @@ class Migration3Callback:
 
     def _migrate_model_config_records(self, cursor: sqlite3.Cursor) -> None:
         """After updating the model config table, we repopulate it."""
-        model_record_migrator = MigrateModelYamlToDb1(cursor)
+        self._logger.info("Migrating model config records from models.yaml to database")
+        model_record_migrator = MigrateModelYamlToDb1(self._app_config, self._logger, cursor)
         model_record_migrator.migrate()
 
 
-def build_migration_3() -> Migration:
+def build_migration_3(app_config: InvokeAIAppConfig, logger: Logger) -> Migration:
     """
     Build the migration from database version 2 to 3.
 
@@ -69,7 +73,7 @@ def build_migration_3() -> Migration:
     migration_3 = Migration(
         from_version=2,
         to_version=3,
-        callback=Migration3Callback(),
+        callback=Migration3Callback(app_config=app_config, logger=logger),
     )
 
     return migration_3

invokeai/app/services/shared/sqlite_migrator/migrations/migration_4.py

@@ -0,0 +1,83 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration4Callback:
+    """Callback to do step 4 of migration."""
+
+    def __call__(self, cursor: sqlite3.Cursor) -> None:  # noqa D102
+        self._create_model_metadata(cursor)
+        self._create_model_tags(cursor)
+        self._create_tags(cursor)
+        self._create_triggers(cursor)
+
+    def _create_model_metadata(self, cursor: sqlite3.Cursor) -> None:
+        """Create the table used to store model metadata downloaded from remote sources."""
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS model_metadata (
+                id TEXT NOT NULL PRIMARY KEY,
+                name TEXT GENERATED ALWAYS AS (json_extract(metadata, '$.name')) VIRTUAL NOT NULL,
+                author TEXT GENERATED ALWAYS AS (json_extract(metadata, '$.author')) VIRTUAL NOT NULL,
+                -- Serialized JSON representation of the whole metadata object,
+                -- which will contain additional fields from subclasses
+                metadata 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')),
+                FOREIGN KEY(id) REFERENCES model_config(id) ON DELETE CASCADE
+            );
+            """
+        )
+
+    def _create_model_tags(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS model_tags (
+                model_id TEXT NOT NULL,
+                tag_id INTEGER NOT NULL,
+                FOREIGN KEY(model_id) REFERENCES model_config(id) ON DELETE CASCADE,
+                FOREIGN KEY(tag_id) REFERENCES tags(tag_id) ON DELETE CASCADE,
+                UNIQUE(model_id,tag_id)
+            );
+            """
+        )
+
+    def _create_tags(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """--sql
+            CREATE TABLE IF NOT EXISTS tags (
+                tag_id INTEGER NOT NULL PRIMARY KEY,
+                tag_text TEXT NOT NULL UNIQUE
+            );
+            """
+        )
+
+    def _create_triggers(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """--sql
+            CREATE TRIGGER IF NOT EXISTS model_metadata_updated_at
+            AFTER UPDATE
+            ON model_metadata FOR EACH ROW
+            BEGIN
+                UPDATE model_metadata SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                    WHERE id = old.id;
+            END;
+            """
+        )
+
+
+def build_migration_4() -> Migration:
+    """
+    Build the migration from database version 3 to 4.
+
+    Adds the tables needed to store model metadata and tags.
+    """
+    migration_4 = Migration(
+        from_version=3,
+        to_version=4,
+        callback=Migration4Callback(),
+    )
+
+    return migration_4

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

@@ -23,7 +23,6 @@ from invokeai.backend.model_manager.config import (
     ModelType,
 )
 from invokeai.backend.model_manager.hash import FastModelHash
-from invokeai.backend.util.logging import InvokeAILogger
 
 ModelsValidator = TypeAdapter(AnyModelConfig)
 
@@ -46,10 +45,9 @@ class MigrateModelYamlToDb1:
     logger: Logger
     cursor: sqlite3.Cursor
 
-    def __init__(self, cursor: sqlite3.Cursor = None) -> None:
-        self.config = InvokeAIAppConfig.get_config()
-        self.config.parse_args()
-        self.logger = InvokeAILogger.get_logger()
+    def __init__(self, config: InvokeAIAppConfig, logger: Logger, cursor: sqlite3.Cursor = None) -> None:
+        self.config = config
+        self.logger = logger
         self.cursor = cursor
 
     def get_yaml(self) -> DictConfig:

invokeai/app/services/workflow_records/default_workflows/ESRGAN Upscaling with Canny ControlNet.json

@@ -1,5 +1,4 @@
 {
-  "id": "6bfa0b3a-7090-4cd9-ad2d-a4b8662b6e71",
   "name": "ESRGAN Upscaling with Canny ControlNet",
   "author": "InvokeAI",
   "description": "Sample workflow for using Upscaling with ControlNet with SD1.5",
@@ -77,12 +76,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 256,
       "position": {
         "x": 1250,
         "y": 1500
-      }
+      },
+      "width": 320,
+      "height": 219
     },
     {
       "id": "d8ace142-c05f-4f1d-8982-88dc7473958d",
@@ -148,12 +147,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 227,
       "position": {
         "x": 700,
         "y": 1375
-      }
+      },
+      "width": 320,
+      "height": 193
     },
     {
       "id": "771bdf6a-0813-4099-a5d8-921a138754d4",
@@ -214,12 +213,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 225,
       "position": {
         "x": 375,
         "y": 1900
-      }
+      },
+      "width": 320,
+      "height": 189
     },
     {
       "id": "f7564dd2-9539-47f2-ac13-190804461f4e",
@@ -315,12 +314,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 340,
       "position": {
         "x": 775,
         "y": 1900
-      }
+      },
+      "width": 320,
+      "height": 295
     },
     {
       "id": "1d887701-df21-4966-ae6e-a7d82307d7bd",
@@ -416,12 +415,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 340,
       "position": {
         "x": 1200,
         "y": 1900
-      }
+      },
+      "width": 320,
+      "height": 293
     },
     {
       "id": "ca1d020c-89a8-4958-880a-016d28775cfa",
@@ -434,7 +433,7 @@
         "notes": "",
         "isIntermediate": true,
         "useCache": true,
-        "version": "1.1.0",
+        "version": "1.1.1",
         "nodePack": "invokeai",
         "inputs": {
           "image": {
@@ -537,12 +536,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 511,
       "position": {
         "x": 1650,
         "y": 1900
-      }
+      },
+      "width": 320,
+      "height": 451
     },
     {
       "id": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
@@ -640,12 +639,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 32,
       "position": {
         "x": 1650,
         "y": 1775
-      }
+      },
+      "width": 320,
+      "height": 24
     },
     {
       "id": "c3737554-8d87-48ff-a6f8-e71d2867f434",
@@ -658,7 +657,7 @@
         "notes": "",
         "isIntermediate": true,
         "useCache": true,
-        "version": "1.5.0",
+        "version": "1.5.1",
         "nodePack": "invokeai",
         "inputs": {
           "positive_conditioning": {
@@ -866,12 +865,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 705,
       "position": {
         "x": 2128.740065979906,
         "y": 1232.6219060454753
-      }
+      },
+      "width": 320,
+      "height": 612
     },
     {
       "id": "3ed9b2ef-f4ec-40a7-94db-92e63b583ec0",
@@ -978,12 +977,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 267,
       "position": {
         "x": 2559.4751127537957,
         "y": 1246.6000376741406
-      }
+      },
+      "width": 320,
+      "height": 224
     },
     {
       "id": "5ca498a4-c8c8-4580-a396-0c984317205d",
@@ -1079,12 +1078,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 32,
       "position": {
         "x": 1650,
         "y": 1675
-      }
+      },
+      "width": 320,
+      "height": 24
     },
     {
       "id": "63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16b",
@@ -1137,12 +1136,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 256,
       "position": {
         "x": 1250,
         "y": 1200
-      }
+      },
+      "width": 320,
+      "height": 219
     },
     {
       "id": "eb8f6f8a-c7b1-4914-806e-045ee2717a35",
@@ -1195,168 +1194,168 @@
           }
         }
       },
-      "width": 320,
-      "height": 32,
       "position": {
         "x": 1650,
         "y": 1600
-      }
+      },
+      "width": 320,
+      "height": 24
     }
   ],
   "edges": [
     {
       "id": "5ca498a4-c8c8-4580-a396-0c984317205d-f50624ce-82bf-41d0-bdf7-8aab11a80d48-collapsed",
+      "type": "collapsed",
       "source": "5ca498a4-c8c8-4580-a396-0c984317205d",
-      "target": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
-      "type": "collapsed"
+      "target": "f50624ce-82bf-41d0-bdf7-8aab11a80d48"
     },
     {
       "id": "eb8f6f8a-c7b1-4914-806e-045ee2717a35-f50624ce-82bf-41d0-bdf7-8aab11a80d48-collapsed",
+      "type": "collapsed",
       "source": "eb8f6f8a-c7b1-4914-806e-045ee2717a35",
-      "target": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
-      "type": "collapsed"
+      "target": "f50624ce-82bf-41d0-bdf7-8aab11a80d48"
     },
     {
       "id": "reactflow__edge-771bdf6a-0813-4099-a5d8-921a138754d4image-f7564dd2-9539-47f2-ac13-190804461f4eimage",
+      "type": "default",
       "source": "771bdf6a-0813-4099-a5d8-921a138754d4",
       "target": "f7564dd2-9539-47f2-ac13-190804461f4e",
-      "type": "default",
       "sourceHandle": "image",
       "targetHandle": "image"
     },
     {
       "id": "reactflow__edge-f7564dd2-9539-47f2-ac13-190804461f4eimage-1d887701-df21-4966-ae6e-a7d82307d7bdimage",
+      "type": "default",
       "source": "f7564dd2-9539-47f2-ac13-190804461f4e",
       "target": "1d887701-df21-4966-ae6e-a7d82307d7bd",
-      "type": "default",
       "sourceHandle": "image",
       "targetHandle": "image"
     },
     {
       "id": "reactflow__edge-5ca498a4-c8c8-4580-a396-0c984317205dwidth-f50624ce-82bf-41d0-bdf7-8aab11a80d48width",
+      "type": "default",
       "source": "5ca498a4-c8c8-4580-a396-0c984317205d",
       "target": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
-      "type": "default",
       "sourceHandle": "width",
       "targetHandle": "width"
     },
     {
       "id": "reactflow__edge-5ca498a4-c8c8-4580-a396-0c984317205dheight-f50624ce-82bf-41d0-bdf7-8aab11a80d48height",
+      "type": "default",
       "source": "5ca498a4-c8c8-4580-a396-0c984317205d",
       "target": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
-      "type": "default",
       "sourceHandle": "height",
       "targetHandle": "height"
     },
     {
       "id": "reactflow__edge-f50624ce-82bf-41d0-bdf7-8aab11a80d48noise-c3737554-8d87-48ff-a6f8-e71d2867f434noise",
+      "type": "default",
       "source": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
       "target": "c3737554-8d87-48ff-a6f8-e71d2867f434",
-      "type": "default",
       "sourceHandle": "noise",
       "targetHandle": "noise"
     },
     {
       "id": "reactflow__edge-5ca498a4-c8c8-4580-a396-0c984317205dlatents-c3737554-8d87-48ff-a6f8-e71d2867f434latents",
+      "type": "default",
       "source": "5ca498a4-c8c8-4580-a396-0c984317205d",
       "target": "c3737554-8d87-48ff-a6f8-e71d2867f434",
-      "type": "default",
       "sourceHandle": "latents",
       "targetHandle": "latents"
     },
     {
       "id": "reactflow__edge-e8bf67fe-67de-4227-87eb-79e86afdfc74conditioning-c3737554-8d87-48ff-a6f8-e71d2867f434negative_conditioning",
+      "type": "default",
       "source": "e8bf67fe-67de-4227-87eb-79e86afdfc74",
       "target": "c3737554-8d87-48ff-a6f8-e71d2867f434",
-      "type": "default",
       "sourceHandle": "conditioning",
       "targetHandle": "negative_conditioning"
     },
     {
       "id": "reactflow__edge-63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16bconditioning-c3737554-8d87-48ff-a6f8-e71d2867f434positive_conditioning",
+      "type": "default",
       "source": "63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16b",
       "target": "c3737554-8d87-48ff-a6f8-e71d2867f434",
-      "type": "default",
       "sourceHandle": "conditioning",
       "targetHandle": "positive_conditioning"
     },
     {
       "id": "reactflow__edge-d8ace142-c05f-4f1d-8982-88dc7473958dclip-63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16bclip",
+      "type": "default",
       "source": "d8ace142-c05f-4f1d-8982-88dc7473958d",
       "target": "63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16b",
-      "type": "default",
       "sourceHandle": "clip",
       "targetHandle": "clip"
     },
     {
       "id": "reactflow__edge-d8ace142-c05f-4f1d-8982-88dc7473958dclip-e8bf67fe-67de-4227-87eb-79e86afdfc74clip",
+      "type": "default",
       "source": "d8ace142-c05f-4f1d-8982-88dc7473958d",
       "target": "e8bf67fe-67de-4227-87eb-79e86afdfc74",
-      "type": "default",
       "sourceHandle": "clip",
       "targetHandle": "clip"
     },
     {
       "id": "reactflow__edge-1d887701-df21-4966-ae6e-a7d82307d7bdimage-ca1d020c-89a8-4958-880a-016d28775cfaimage",
+      "type": "default",
       "source": "1d887701-df21-4966-ae6e-a7d82307d7bd",
       "target": "ca1d020c-89a8-4958-880a-016d28775cfa",
-      "type": "default",
       "sourceHandle": "image",
       "targetHandle": "image"
     },
     {
       "id": "reactflow__edge-ca1d020c-89a8-4958-880a-016d28775cfacontrol-c3737554-8d87-48ff-a6f8-e71d2867f434control",
+      "type": "default",
       "source": "ca1d020c-89a8-4958-880a-016d28775cfa",
       "target": "c3737554-8d87-48ff-a6f8-e71d2867f434",
-      "type": "default",
       "sourceHandle": "control",
       "targetHandle": "control"
     },
     {
       "id": "reactflow__edge-c3737554-8d87-48ff-a6f8-e71d2867f434latents-3ed9b2ef-f4ec-40a7-94db-92e63b583ec0latents",
+      "type": "default",
       "source": "c3737554-8d87-48ff-a6f8-e71d2867f434",
       "target": "3ed9b2ef-f4ec-40a7-94db-92e63b583ec0",
-      "type": "default",
       "sourceHandle": "latents",
       "targetHandle": "latents"
     },
     {
       "id": "reactflow__edge-d8ace142-c05f-4f1d-8982-88dc7473958dvae-3ed9b2ef-f4ec-40a7-94db-92e63b583ec0vae",
+      "type": "default",
       "source": "d8ace142-c05f-4f1d-8982-88dc7473958d",
       "target": "3ed9b2ef-f4ec-40a7-94db-92e63b583ec0",
-      "type": "default",
       "sourceHandle": "vae",
       "targetHandle": "vae"
     },
     {
       "id": "reactflow__edge-f7564dd2-9539-47f2-ac13-190804461f4eimage-5ca498a4-c8c8-4580-a396-0c984317205dimage",
+      "type": "default",
       "source": "f7564dd2-9539-47f2-ac13-190804461f4e",
       "target": "5ca498a4-c8c8-4580-a396-0c984317205d",
-      "type": "default",
       "sourceHandle": "image",
       "targetHandle": "image"
     },
     {
       "id": "reactflow__edge-d8ace142-c05f-4f1d-8982-88dc7473958dunet-c3737554-8d87-48ff-a6f8-e71d2867f434unet",
+      "type": "default",
       "source": "d8ace142-c05f-4f1d-8982-88dc7473958d",
       "target": "c3737554-8d87-48ff-a6f8-e71d2867f434",
-      "type": "default",
       "sourceHandle": "unet",
       "targetHandle": "unet"
     },
     {
       "id": "reactflow__edge-d8ace142-c05f-4f1d-8982-88dc7473958dvae-5ca498a4-c8c8-4580-a396-0c984317205dvae",
+      "type": "default",
       "source": "d8ace142-c05f-4f1d-8982-88dc7473958d",
       "target": "5ca498a4-c8c8-4580-a396-0c984317205d",
-      "type": "default",
       "sourceHandle": "vae",
       "targetHandle": "vae"
     },
     {
       "id": "reactflow__edge-eb8f6f8a-c7b1-4914-806e-045ee2717a35value-f50624ce-82bf-41d0-bdf7-8aab11a80d48seed",
+      "type": "default",
       "source": "eb8f6f8a-c7b1-4914-806e-045ee2717a35",
       "target": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
-      "type": "default",
       "sourceHandle": "value",
       "targetHandle": "seed"
     }

invokeai/app/services/workflow_records/default_workflows/Multi ControlNet (Canny & Depth).json

@@ -1,5 +1,4 @@
 {
-  "id": "1e385b84-86f8-452e-9697-9e5abed20518",
   "name": "Multi ControlNet (Canny & Depth)",
   "author": "InvokeAI",
   "description": "A sample workflow using canny & depth ControlNets to guide the generation process. ",
@@ -93,12 +92,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 225,
       "position": {
         "x": 3625,
         "y": -75
-      }
+      },
+      "width": 320,
+      "height": 189
     },
     {
       "id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
@@ -111,7 +110,7 @@
         "notes": "",
         "isIntermediate": true,
         "useCache": true,
-        "version": "1.1.0",
+        "version": "1.1.1",
         "nodePack": "invokeai",
         "inputs": {
           "image": {
@@ -214,12 +213,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 511,
       "position": {
         "x": 4477.604342844504,
         "y": -49.39005411272677
-      }
+      },
+      "width": 320,
+      "height": 451
     },
     {
       "id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
@@ -272,12 +271,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 256,
       "position": {
         "x": 4075,
         "y": -825
-      }
+      },
+      "width": 320,
+      "height": 219
     },
     {
       "id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
@@ -343,12 +342,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 227,
       "position": {
         "x": 3600,
         "y": -1000
-      }
+      },
+      "width": 320,
+      "height": 193
     },
     {
       "id": "7ce68934-3419-42d4-ac70-82cfc9397306",
@@ -401,12 +400,12 @@
           }
         }
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invokeai/app/services/workflow_records/default_workflows/SDXL Text to Image.json

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invokeai/app/services/workflow_records/default_workflows/Text to Image - SD1.5.json

@@ -84,12 +84,12 @@
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       "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
+      "type": "default",
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "type": "default",
       "sourceHandle": "vae",
       "targetHandle": "vae"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json

@@ -25,10 +25,9 @@
     }
   ],
   "meta": {
-    "version": "2.0.0",
-    "category": "default"
+    "category": "default",
+    "version": "2.0.0"
   },
-  "id": "a9d70c39-4cdd-4176-9942-8ff3fe32d3b1",
   "nodes": [
     {
       "id": "85b77bb2-c67a-416a-b3e8-291abe746c44",
@@ -80,12 +79,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 256,
       "position": {
         "x": 3425,
         "y": -300
-      }
+      },
+      "width": 320,
+      "height": 219
     },
     {
       "id": "24e9d7ed-4836-4ec4-8f9e-e747721f9818",
@@ -150,12 +149,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 227,
       "position": {
         "x": 2500,
         "y": -600
-      }
+      },
+      "width": 320,
+      "height": 193
     },
     {
       "id": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
@@ -243,12 +242,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 252,
       "position": {
         "x": 2975,
         "y": -600
-      }
+      },
+      "width": 320,
+      "height": 218
     },
     {
       "id": "c3fa6872-2599-4a82-a596-b3446a66cf8b",
@@ -300,12 +299,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 256,
       "position": {
         "x": 3425,
         "y": -575
-      }
+      },
+      "width": 320,
+      "height": 219
     },
     {
       "id": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
@@ -318,7 +317,7 @@
         "notes": "",
         "isIntermediate": true,
         "useCache": true,
-        "version": "1.5.0",
+        "version": "1.5.1",
         "inputs": {
           "positive_conditioning": {
             "id": "025ff44b-c4c6-4339-91b4-5f461e2cadc5",
@@ -525,12 +524,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 705,
       "position": {
         "x": 3975,
         "y": -575
-      }
+      },
+      "width": 320,
+      "height": 612
     },
     {
       "id": "ea18915f-2c5b-4569-b725-8e9e9122e8d3",
@@ -627,12 +626,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 32,
       "position": {
         "x": 3425,
         "y": 75
-      }
+      },
+      "width": 320,
+      "height": 24
     },
     {
       "id": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953",
@@ -685,12 +684,12 @@
           }
         }
       },
-      "width": 320,
-      "height": 32,
       "position": {
         "x": 3425,
         "y": 0
-      }
+      },
+      "width": 320,
+      "height": 24
     },
     {
       "id": "a9683c0a-6b1f-4a5e-8187-c57e764b3400",
@@ -796,106 +795,106 @@
           }
         }
       },
-      "width": 320,
-      "height": 267,
       "position": {
         "x": 4450,
         "y": -550
-      }
+      },
+      "width": 320,
+      "height": 224
     }
   ],
   "edges": [
     {
       "id": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953-ea18915f-2c5b-4569-b725-8e9e9122e8d3-collapsed",
+      "type": "collapsed",
       "source": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953",
-      "target": "ea18915f-2c5b-4569-b725-8e9e9122e8d3",
-      "type": "collapsed"
+      "target": "ea18915f-2c5b-4569-b725-8e9e9122e8d3"
     },
     {
       "id": "reactflow__edge-24e9d7ed-4836-4ec4-8f9e-e747721f9818clip-c41e705b-f2e3-4d1a-83c4-e34bb9344966clip",
+      "type": "default",
       "source": "24e9d7ed-4836-4ec4-8f9e-e747721f9818",
       "target": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
-      "type": "default",
       "sourceHandle": "clip",
       "targetHandle": "clip"
     },
     {
       "id": "reactflow__edge-c41e705b-f2e3-4d1a-83c4-e34bb9344966clip-c3fa6872-2599-4a82-a596-b3446a66cf8bclip",
+      "type": "default",
       "source": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
       "target": "c3fa6872-2599-4a82-a596-b3446a66cf8b",
-      "type": "default",
       "sourceHandle": "clip",
       "targetHandle": "clip"
     },
     {
       "id": "reactflow__edge-24e9d7ed-4836-4ec4-8f9e-e747721f9818unet-c41e705b-f2e3-4d1a-83c4-e34bb9344966unet",
+      "type": "default",
       "source": "24e9d7ed-4836-4ec4-8f9e-e747721f9818",
       "target": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
-      "type": "default",
       "sourceHandle": "unet",
       "targetHandle": "unet"
     },
     {
       "id": "reactflow__edge-c41e705b-f2e3-4d1a-83c4-e34bb9344966unet-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63unet",
+      "type": "default",
       "source": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
       "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
       "sourceHandle": "unet",
       "targetHandle": "unet"
     },
     {
       "id": "reactflow__edge-85b77bb2-c67a-416a-b3e8-291abe746c44conditioning-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63negative_conditioning",
+      "type": "default",
       "source": "85b77bb2-c67a-416a-b3e8-291abe746c44",
       "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
       "sourceHandle": "conditioning",
       "targetHandle": "negative_conditioning"
     },
     {
       "id": "reactflow__edge-c3fa6872-2599-4a82-a596-b3446a66cf8bconditioning-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63positive_conditioning",
+      "type": "default",
       "source": "c3fa6872-2599-4a82-a596-b3446a66cf8b",
       "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
       "sourceHandle": "conditioning",
       "targetHandle": "positive_conditioning"
     },
     {
       "id": "reactflow__edge-ea18915f-2c5b-4569-b725-8e9e9122e8d3noise-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63noise",
+      "type": "default",
       "source": "ea18915f-2c5b-4569-b725-8e9e9122e8d3",
       "target": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
-      "type": "default",
       "sourceHandle": "noise",
       "targetHandle": "noise"
     },
     {
       "id": "reactflow__edge-6fd74a17-6065-47a5-b48b-f4e2b8fa7953value-ea18915f-2c5b-4569-b725-8e9e9122e8d3seed",
+      "type": "default",
       "source": "6fd74a17-6065-47a5-b48b-f4e2b8fa7953",
       "target": "ea18915f-2c5b-4569-b725-8e9e9122e8d3",
-      "type": "default",
       "sourceHandle": "value",
       "targetHandle": "seed"
     },
     {
       "id": "reactflow__edge-ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63latents-a9683c0a-6b1f-4a5e-8187-c57e764b3400latents",
+      "type": "default",
       "source": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63",
       "target": "a9683c0a-6b1f-4a5e-8187-c57e764b3400",
-      "type": "default",
       "sourceHandle": "latents",
       "targetHandle": "latents"
     },
     {
       "id": "reactflow__edge-24e9d7ed-4836-4ec4-8f9e-e747721f9818vae-a9683c0a-6b1f-4a5e-8187-c57e764b3400vae",
+      "type": "default",
       "source": "24e9d7ed-4836-4ec4-8f9e-e747721f9818",
       "target": "a9683c0a-6b1f-4a5e-8187-c57e764b3400",
-      "type": "default",
       "sourceHandle": "vae",
       "targetHandle": "vae"
     },
     {
       "id": "reactflow__edge-c41e705b-f2e3-4d1a-83c4-e34bb9344966clip-85b77bb2-c67a-416a-b3e8-291abe746c44clip",
+      "type": "default",
       "source": "c41e705b-f2e3-4d1a-83c4-e34bb9344966",
       "target": "85b77bb2-c67a-416a-b3e8-291abe746c44",
-      "type": "default",
       "sourceHandle": "clip",
       "targetHandle": "clip"
     }

invokeai/backend/install/model_install_backend.py

@@ -283,11 +283,16 @@ class ModelInstall(object):
 
     def _remove_installed(self, model_list: List[str]):
         all_models = self.all_models()
+        models_to_remove = []
+
         for path in model_list:
             key = self.reverse_paths.get(path)
             if key and all_models[key].installed:
-                logger.warning(f"{path} already installed. Skipping.")
-                model_list.remove(path)
+                models_to_remove.append(path)
+
+        for path in models_to_remove:
+            logger.warning(f"{path} already installed. Skipping")
+            model_list.remove(path)
 
     def _add_required_models(self, model_list: List[str]):
         additional_models = []

invokeai/backend/model_management/lora.py

@@ -113,7 +113,7 @@ class ModelPatcher:
                     for layer_key, layer in lora.layers.items():
                         if not layer_key.startswith(prefix):
                             continue
-
+                        print("test")
                         # TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This
                         # should be improved in the following ways:
                         # 1. The key mapping could be more-efficiently pre-computed. This would save time every time a

invokeai/backend/model_management/model_manager.py

@@ -759,7 +759,7 @@ class ModelManager(object):
         model_type: ModelType,
         new_name: Optional[str] = None,
         new_base: Optional[BaseModelType] = None,
-    ):
+    ) -> None:
         """
         Rename or rebase a model.
         """
@@ -781,6 +781,9 @@ class ModelManager(object):
 
         # if this is a model file/directory that we manage ourselves, we need to move it
         if old_path.is_relative_to(self.app_config.models_path):
+            # keep the suffix!
+            if old_path.is_file():
+                new_name = Path(new_name).with_suffix(old_path.suffix).as_posix()
             new_path = self.resolve_model_path(
                 Path(
                     BaseModelType(new_base).value,

invokeai/backend/model_manager/__init__.py

@@ -6,6 +6,7 @@ from .config import (
     InvalidModelConfigException,
     ModelConfigFactory,
     ModelFormat,
+    ModelRepoVariant,
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
@@ -15,15 +16,16 @@ from .probe import ModelProbe
 from .search import ModelSearch
 
 __all__ = [
-    "ModelProbe",
-    "ModelSearch",
+    "AnyModelConfig",
+    "BaseModelType",
+    "ModelRepoVariant",
     "InvalidModelConfigException",
     "ModelConfigFactory",
-    "BaseModelType",
-    "ModelType",
-    "SubModelType",
-    "ModelVariantType",
     "ModelFormat",
+    "ModelProbe",
+    "ModelSearch",
+    "ModelType",
+    "ModelVariantType",
     "SchedulerPredictionType",
-    "AnyModelConfig",
+    "SubModelType",
 ]

invokeai/backend/model_manager/config.py

@@ -99,6 +99,17 @@ class SchedulerPredictionType(str, Enum):
     Sample = "sample"
 
 
+class ModelRepoVariant(str, Enum):
+    """Various hugging face variants on the diffusers format."""
+
+    DEFAULT = "default"  # model files without "fp16" or other qualifier
+    FP16 = "fp16"
+    FP32 = "fp32"
+    ONNX = "onnx"
+    OPENVINO = "openvino"
+    FLAX = "flax"
+
+
 class ModelConfigBase(BaseModel):
     """Base class for model configuration information."""
 

invokeai/backend/model_manager/metadata/__init__.py

@@ -0,0 +1,50 @@
+"""
+Initialization file for invokeai.backend.model_manager.metadata
+
+Usage:
+
+from invokeai.backend.model_manager.metadata import(
+   AnyModelRepoMetadata,
+   CommercialUsage,
+   LicenseRestrictions,
+   HuggingFaceMetadata,
+   CivitaiMetadata,
+)
+
+from invokeai.backend.model_manager.metadata.fetch import CivitaiMetadataFetch
+
+data = CivitaiMetadataFetch().from_url("https://civitai.com/models/206883/split")
+assert isinstance(data, CivitaiMetadata)
+if data.allow_commercial_use:
+   print("Commercial use of this model is allowed")
+"""
+from .fetch import CivitaiMetadataFetch, HuggingFaceMetadataFetch
+from .metadata_base import (
+    AnyModelRepoMetadata,
+    AnyModelRepoMetadataValidator,
+    BaseMetadata,
+    CivitaiMetadata,
+    CommercialUsage,
+    HuggingFaceMetadata,
+    LicenseRestrictions,
+    ModelMetadataWithFiles,
+    RemoteModelFile,
+    UnknownMetadataException,
+)
+from .metadata_store import ModelMetadataStore
+
+__all__ = [
+    "AnyModelRepoMetadata",
+    "AnyModelRepoMetadataValidator",
+    "CivitaiMetadata",
+    "CivitaiMetadataFetch",
+    "CommercialUsage",
+    "HuggingFaceMetadata",
+    "HuggingFaceMetadataFetch",
+    "LicenseRestrictions",
+    "ModelMetadataStore",
+    "BaseMetadata",
+    "ModelMetadataWithFiles",
+    "RemoteModelFile",
+    "UnknownMetadataException",
+]

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

@@ -0,0 +1,21 @@
+"""
+Initialization file for invokeai.backend.model_manager.metadata.fetch
+
+Usage:
+from invokeai.backend.model_manager.metadata.fetch import (
+    CivitaiMetadataFetch,
+    HuggingFaceMetadataFetch,
+)
+from invokeai.backend.model_manager.metadata import CivitaiMetadata
+
+data = CivitaiMetadataFetch().from_url("https://civitai.com/models/206883/split")
+assert isinstance(data, CivitaiMetadata)
+if data.allow_commercial_use:
+   print("Commercial use of this model is allowed")
+"""
+
+from .civitai import CivitaiMetadataFetch
+from .fetch_base import ModelMetadataFetchBase
+from .huggingface import HuggingFaceMetadataFetch
+
+__all__ = ["ModelMetadataFetchBase", "CivitaiMetadataFetch", "HuggingFaceMetadataFetch"]

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

@@ -0,0 +1,187 @@
+# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
+
+"""
+This module fetches model metadata objects from the Civitai model repository.
+In addition to the `from_url()` and `from_id()` methods inherited from the
+`ModelMetadataFetchBase` base class.
+
+Civitai has two separate ID spaces: a model ID and a version ID. The
+version ID corresponds to a specific model, and is the ID accepted by
+`from_id()`. The model ID corresponds to a family of related models,
+such as different training checkpoints or 16 vs 32-bit versions. The
+`from_civitai_modelid()` method will accept a model ID and return the
+metadata from the default version within this model set. The default
+version is the same as what the user sees when they click on a model's
+thumbnail.
+
+Usage:
+
+from invokeai.backend.model_manager.metadata.fetch import CivitaiMetadataFetch
+
+fetcher = CivitaiMetadataFetch()
+metadata = fetcher.from_url("https://civitai.com/models/206883/split")
+print(metadata.trained_words)
+"""
+
+import re
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import requests
+from pydantic.networks import AnyHttpUrl
+from requests.sessions import Session
+
+from ..metadata_base import (
+    AnyModelRepoMetadata,
+    CivitaiMetadata,
+    CommercialUsage,
+    LicenseRestrictions,
+    RemoteModelFile,
+    UnknownMetadataException,
+)
+from .fetch_base import ModelMetadataFetchBase
+
+CIVITAI_MODEL_PAGE_RE = r"https?://civitai.com/models/(\d+)"
+CIVITAI_VERSION_PAGE_RE = r"https?://civitai.com/models/(\d+)\?modelVersionId=(\d+)"
+CIVITAI_DOWNLOAD_RE = r"https?://civitai.com/api/download/models/(\d+)"
+
+CIVITAI_VERSION_ENDPOINT = "https://civitai.com/api/v1/model-versions/"
+CIVITAI_MODEL_ENDPOINT = "https://civitai.com/api/v1/models/"
+
+
+class CivitaiMetadataFetch(ModelMetadataFetchBase):
+    """Fetch model metadata from Civitai."""
+
+    def __init__(self, session: Optional[Session] = None):
+        """
+        Initialize the fetcher with an optional requests.sessions.Session object.
+
+        By providing a configurable Session object, we can support unit tests on
+        this module without an internet connection.
+        """
+        self._requests = session or requests.Session()
+
+    def from_url(self, url: AnyHttpUrl) -> AnyModelRepoMetadata:
+        """
+        Given a URL to a CivitAI model or version page, return a ModelMetadata object.
+
+        In the event that the URL points to a model page without the particular version
+        indicated, the default model version is returned. Otherwise, the requested version
+        is returned.
+        """
+        if match := re.match(CIVITAI_VERSION_PAGE_RE, str(url), re.IGNORECASE):
+            model_id = match.group(1)
+            version_id = match.group(2)
+            return self.from_civitai_versionid(int(version_id), int(model_id))
+        elif match := re.match(CIVITAI_MODEL_PAGE_RE, str(url), re.IGNORECASE):
+            model_id = match.group(1)
+            return self.from_civitai_modelid(int(model_id))
+        elif match := re.match(CIVITAI_DOWNLOAD_RE, str(url), re.IGNORECASE):
+            version_id = match.group(1)
+            return self.from_civitai_versionid(int(version_id))
+        raise UnknownMetadataException("The url '{url}' does not match any known Civitai URL patterns")
+
+    def from_id(self, id: str) -> AnyModelRepoMetadata:
+        """
+        Given a Civitai model version ID, return a ModelRepoMetadata object.
+
+        May raise an `UnknownMetadataException`.
+        """
+        return self.from_civitai_versionid(int(id))
+
+    def from_civitai_modelid(self, model_id: int) -> CivitaiMetadata:
+        """
+        Return metadata from the default version of the indicated model.
+
+        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)
+
+    def _from_model_json(self, model_json: Dict[str, Any], version_id: Optional[int] = None) -> CivitaiMetadata:
+        try:
+            version_id = version_id or model_json["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]
+        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")]
+        assert len(primary) == 1
+        primary_file = primary[0]
+
+        url = primary_file["downloadUrl"]
+        if "?" not in url:  # work around apparent bug in civitai api
+            metadata_string = ""
+            for key, value in primary_file["metadata"].items():
+                if not value:
+                    continue
+                metadata_string += f"&{key}={value}"
+            url = url + f"?type={primary_file['type']}{metadata_string}"
+        model_files = [
+            RemoteModelFile(
+                url=url,
+                path=Path(primary_file["name"]),
+                size=int(primary_file["sizeKB"] * 1024),
+                sha256=primary_file["hashes"]["SHA256"],
+            )
+        ]
+        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(model_json["allowCommercialUse"]),
+                AllowDerivatives=model_json["allowDerivatives"],
+                AllowDifferentLicense=model_json["allowDifferentLicense"],
+            ),
+        )
+
+    def from_civitai_versionid(self, version_id: int, model_id: Optional[int] = None) -> CivitaiMetadata:
+        """
+        Return a CivitaiMetadata object given a model version id.
+
+        May raise an `UnknownMetadataException`.
+        """
+        if model_id is None:
+            version_url = CIVITAI_VERSION_ENDPOINT + str(version_id)
+            version = self._requests.get(version_url).json()
+            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)
+
+    @classmethod
+    def from_json(cls, json: str) -> CivitaiMetadata:
+        """Given the JSON representation of the metadata, return the corresponding Pydantic object."""
+        metadata = CivitaiMetadata.model_validate_json(json)
+        return metadata
+
+
+def _fix_timezone(date: str) -> str:
+    return re.sub(r"Z$", "+00:00", date)

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

@@ -0,0 +1,61 @@
+# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
+
+"""
+This module is the base class for subclasses that fetch metadata from model repositories
+
+Usage:
+
+from invokeai.backend.model_manager.metadata.fetch import CivitAIMetadataFetch
+
+fetcher = CivitaiMetadataFetch()
+metadata = fetcher.from_url("https://civitai.com/models/206883/split")
+print(metadata.trained_words)
+"""
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from pydantic.networks import AnyHttpUrl
+from requests.sessions import Session
+
+from ..metadata_base import AnyModelRepoMetadata, AnyModelRepoMetadataValidator
+
+
+class ModelMetadataFetchBase(ABC):
+    """Fetch metadata from remote generative model repositories."""
+
+    @abstractmethod
+    def __init__(self, session: Optional[Session] = None):
+        """
+        Initialize the fetcher with an optional requests.sessions.Session object.
+
+        By providing a configurable Session object, we can support unit tests on
+        this module without an internet connection.
+        """
+        pass
+
+    @abstractmethod
+    def from_url(self, url: AnyHttpUrl) -> AnyModelRepoMetadata:
+        """
+        Given a URL to a model repository, return a ModelMetadata object.
+
+        This method will raise a `UnknownMetadataException`
+        in the event that the requested model metadata is not found at the provided location.
+        """
+        pass
+
+    @abstractmethod
+    def from_id(self, id: str) -> AnyModelRepoMetadata:
+        """
+        Given an ID for a model, return a ModelMetadata object.
+
+        This method will raise a `UnknownMetadataException`
+        in the event that the requested model's metadata is not found at the provided id.
+        """
+        pass
+
+    @classmethod
+    def from_json(cls, json: str) -> AnyModelRepoMetadata:
+        """Given the JSON representation of the metadata, return the corresponding Pydantic object."""
+        metadata = AnyModelRepoMetadataValidator.validate_json(json)
+        return metadata

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

@@ -0,0 +1,92 @@
+# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
+
+"""
+This module fetches model metadata objects from the HuggingFace model repository,
+using either a `repo_id` or the model page URL.
+
+Usage:
+
+from invokeai.backend.model_manager.metadata.fetch import HuggingFaceMetadataFetch
+
+fetcher = HuggingFaceMetadataFetch()
+metadata = fetcher.from_url("https://huggingface.co/stabilityai/sdxl-turbo")
+print(metadata.tags)
+"""
+
+import re
+from pathlib import Path
+from typing import Optional
+
+import requests
+from huggingface_hub import HfApi, configure_http_backend, hf_hub_url
+from huggingface_hub.utils._errors import RepositoryNotFoundError
+from pydantic.networks import AnyHttpUrl
+from requests.sessions import Session
+
+from ..metadata_base import (
+    AnyModelRepoMetadata,
+    HuggingFaceMetadata,
+    RemoteModelFile,
+    UnknownMetadataException,
+)
+from .fetch_base import ModelMetadataFetchBase
+
+HF_MODEL_RE = r"https?://huggingface.co/([\w\-.]+/[\w\-.]+)"
+
+
+class HuggingFaceMetadataFetch(ModelMetadataFetchBase):
+    """Fetch model metadata from HuggingFace."""
+
+    def __init__(self, session: Optional[Session] = None):
+        """
+        Initialize the fetcher with an optional requests.sessions.Session object.
+
+        By providing a configurable Session object, we can support unit tests on
+        this module without an internet connection.
+        """
+        self._requests = session or requests.Session()
+        configure_http_backend(backend_factory=lambda: self._requests)
+
+    @classmethod
+    def from_json(cls, json: str) -> HuggingFaceMetadata:
+        """Given the JSON representation of the metadata, return the corresponding Pydantic object."""
+        metadata = HuggingFaceMetadata.model_validate_json(json)
+        return metadata
+
+    def from_id(self, id: str) -> AnyModelRepoMetadata:
+        """Return a HuggingFaceMetadata object given the model's repo_id."""
+        try:
+            model_info = HfApi().model_info(repo_id=id, files_metadata=True)
+        except RepositoryNotFoundError as excp:
+            raise UnknownMetadataException(f"'{id}' not found. See trace for details.") from excp
+
+        _, 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=[
+                RemoteModelFile(
+                    url=hf_hub_url(id, x.rfilename),
+                    path=Path(name, x.rfilename),
+                    size=x.size,
+                    sha256=x.lfs.get("sha256") if x.lfs else None,
+                )
+                for x in model_info.siblings
+            ],
+        )
+
+    def from_url(self, url: AnyHttpUrl) -> AnyModelRepoMetadata:
+        """
+        Return a HuggingFaceMetadata object given the model's web page URL.
+
+        In the case of an invalid or missing URL, raises a ModelNotFound exception.
+        """
+        if match := re.match(HF_MODEL_RE, str(url), re.IGNORECASE):
+            repo_id = match.group(1)
+            return self.from_id(repo_id)
+        else:
+            raise UnknownMetadataException(f"'{url}' does not look like a HuggingFace model page")

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -0,0 +1,202 @@
+# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
+
+"""This module defines core text-to-image model metadata fields.
+
+Metadata comprises any descriptive information that is not essential
+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.
+
+Note that the "name" and "description" are also present in `config`
+records. This is intentional. The config record fields are intended to
+be editable by the user as a form of customization. The metadata
+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 huggingface_hub import configure_http_backend, hf_hub_url
+from pydantic import BaseModel, Field, TypeAdapter
+from pydantic.networks import AnyHttpUrl
+from requests.sessions import Session
+from typing_extensions import Annotated
+
+from invokeai.backend.model_manager import ModelRepoVariant
+
+from ..util import select_hf_files
+
+
+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: CommercialUsage = Field(
+        description="Type of commercial use allowed or 'No' if no commercial use is allowed.", default_factory=set
+    )
+
+
+class RemoteModelFile(BaseModel):
+    """Information about a downloadable file that forms part of a model."""
+
+    url: AnyHttpUrl = Field(description="The url to download this model file")
+    path: Path = Field(description="The path to the file, relative to the model root")
+    size: int = Field(description="The size of this file, in bytes")
+    sha256: Optional[str] = Field(description="SHA256 hash of this model (not always available)", default=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: Set[str] = Field(description="tags provided by model source")
+
+
+class BaseMetadata(ModelMetadataBase):
+    """Adds typing data for discriminated union."""
+
+    type: Literal["basemetadata"] = "basemetadata"
+
+
+class ModelMetadataWithFiles(ModelMetadataBase):
+    """Base class for metadata that contains a list of downloadable model file(s)."""
+
+    files: List[RemoteModelFile] = Field(description="model files and their sizes", default_factory=list)
+
+    def download_urls(
+        self,
+        variant: Optional[ModelRepoVariant] = None,
+        subfolder: Optional[Path] = None,
+        session: Optional[Session] = None,
+    ) -> List[RemoteModelFile]:
+        """
+        Return a list of URLs needed to download the model.
+
+        :param variant: Return files needed to reconstruct the indicated variant (e.g. ModelRepoVariant('fp16'))
+        :param subfolder: Return files in the designated subfolder only
+        :param session: A request.Session object for offline testing
+
+        Note that the "variant" and "subfolder" concepts currently only apply to HuggingFace.
+        However Civitai does have fields for the precision and format of its models, and may
+        provide variant selection criteria in the future.
+        """
+        return self.files
+
+
+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."""
+        return self.restrictions.AllowCommercialUse != CommercialUsage("None")
+
+    @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
+
+
+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")
+
+    def download_urls(
+        self,
+        variant: Optional[ModelRepoVariant] = None,
+        subfolder: Optional[Path] = None,
+        session: Optional[Session] = None,
+    ) -> List[RemoteModelFile]:
+        """
+        Return list of downloadable files, filtering by variant and subfolder, if any.
+
+        :param variant: Return model files needed to reconstruct the indicated variant
+        :param subfolder: Return model files from the designated subfolder only
+        :param session: A request.Session object used for internet-free testing
+
+        Note that there is special variant-filtering behavior here:
+        When the fp16 variant is requested and not available, the
+        full-precision model is returned.
+        """
+        session = session or Session()
+        configure_http_backend(backend_factory=lambda: session)  # used in testing
+
+        paths = select_hf_files.filter_files(
+            [x.path for x in self.files], variant, subfolder
+        )  #  all files in the model
+        prefix = f"{subfolder}/" if subfolder else ""
+
+        # 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)
+            resp = session.get(url)
+            resp.raise_for_status()
+            submodels = resp.json()
+            paths = [Path(subfolder or "", x) for x in paths if Path(x).parent.as_posix() in submodels]
+            paths.insert(0, Path(f"{prefix}model_index.json"))
+
+        return [x for x in self.files if x.path in paths]
+
+
+AnyModelRepoMetadata = Annotated[Union[BaseMetadata, HuggingFaceMetadata, CivitaiMetadata], Field(discriminator="type")]
+AnyModelRepoMetadataValidator = TypeAdapter(AnyModelRepoMetadata)

invokeai/backend/model_manager/metadata/metadata_store.py

@@ -0,0 +1,221 @@
+# 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

@@ -496,9 +496,9 @@ class PipelineFolderProbe(FolderProbeBase):
     def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         with open(self.model_path / "scheduler" / "scheduler_config.json", "r") as file:
             scheduler_conf = json.load(file)
-        if scheduler_conf["prediction_type"] == "v_prediction":
+        if scheduler_conf.get("prediction_type", "epsilon") == "v_prediction":
             return SchedulerPredictionType.VPrediction
-        elif scheduler_conf["prediction_type"] == "epsilon":
+        elif scheduler_conf.get("prediction_type", "epsilon") == "epsilon":
             return SchedulerPredictionType.Epsilon
         else:
             raise InvalidModelConfigException("Unknown scheduler prediction type: {scheduler_conf['prediction_type']}")

invokeai/backend/model_manager/util/select_hf_files.py

@@ -0,0 +1,132 @@
+# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
+"""
+Select the files from a HuggingFace repository needed for a particular model variant.
+
+Usage:
+```
+from invokeai.backend.model_manager.util.select_hf_files import select_hf_model_files
+from invokeai.backend.model_manager.metadata.fetch import HuggingFaceMetadataFetch
+
+metadata = HuggingFaceMetadataFetch().from_url("https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0")
+files_to_download = select_hf_model_files(metadata.files, variant='onnx')
+```
+"""
+
+import re
+from pathlib import Path
+from typing import Dict, List, Optional, Set
+
+from ..config import ModelRepoVariant
+
+
+def filter_files(
+    files: List[Path],
+    variant: Optional[ModelRepoVariant] = None,
+    subfolder: Optional[Path] = None,
+) -> List[Path]:
+    """
+    Take a list of files in a HuggingFace repo root and return paths to files needed to load the model.
+
+    :param files: List of files relative to the repo root.
+    :param subfolder: Filter by the indicated subfolder.
+    :param variant: Filter by files belonging to a particular variant, such as fp16.
+
+    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
+    paths: List[Path] = []
+
+    # Start by filtering on model file extensions, discarding images, docs, etc
+    for file in files:
+        if file.name.endswith((".json", ".txt")):
+            paths.append(file)
+        elif file.name.endswith(("learned_embeds.bin", "ip_adapter.bin", "lora_weights.safetensors")):
+            paths.append(file)
+        # BRITTLENESS WARNING!!
+        # Diffusers models always seem to have "model" in their name, and the regex filter below is applied to avoid
+        # downloading random checkpoints that might also be in the repo. However there is no guarantee
+        # that a checkpoint doesn't contain "model" in its name, and no guarantee that future diffusers models
+        # will adhere to this naming convention, so this is an area of brittleness.
+        elif re.search(r"model(\.[^.]+)?\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
+            paths.append(file)
+
+    # limit search to subfolder if requested
+    if subfolder:
+        paths = [x for x in paths if x.parent == Path(subfolder)]
+
+    # _filter_by_variant uniquifies the paths and returns a set
+    return sorted(_filter_by_variant(paths, variant))
+
+
+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] = {}
+    for path in files:
+        if path.suffix == ".onnx":
+            if variant == ModelRepoVariant.ONNX:
+                result.add(path)
+
+        elif "openvino_model" in path.name:
+            if variant == ModelRepoVariant.OPENVINO:
+                result.add(path)
+
+        elif "flax_model" in path.name:
+            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 [
+            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
+
+            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
+
+        else:
+            continue
+
+    for v in basenames.values():
+        result.add(v)
+
+    # 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 not any(variant.value in x.name for x in result)
+    ):
+        return set()
+
+    # Prune folders that contain just a `config.json`. This happens when
+    # the requested variant (e.g. "onnx") is missing
+    directories: Dict[Path, int] = {}
+    for x in result:
+        if not x.parent:
+            continue
+        directories[x.parent] = directories.get(x.parent, 0) + 1
+
+    return {x for x in result if directories[x.parent] > 1 or x.name != "config.json"}

invokeai/backend/util/devices.py

@@ -34,18 +34,23 @@ def choose_precision(device: torch.device) -> str:
     if device.type == "cuda":
         device_name = torch.cuda.get_device_name(device)
         if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
-            return "float16"
+            if config.precision == "bfloat16":
+                return "bfloat16"
+            else:
+                return "float16"
     elif device.type == "mps":
         return "float16"
     return "float32"
 
 
 def torch_dtype(device: torch.device) -> torch.dtype:
-    if config.full_precision:
-        return torch.float32
-    if choose_precision(device) == "float16":
-        return torch.bfloat16 if device.type == "cuda" else torch.float16
+    precision = choose_precision(device)
+    if precision == "float16":
+        return torch.float16
+    if precision == "bfloat16":
+        return torch.bfloat16
     else:
+        # "auto", "autocast", "float32"
         return torch.float32
 
 

invokeai/frontend/web/.eslintrc.js

@@ -93,6 +93,27 @@ module.exports = {
     '@typescript-eslint/no-import-type-side-effects': 'error',
     'simple-import-sort/imports': 'error',
     'simple-import-sort/exports': 'error',
+    // Prefer @invoke-ai/ui components over chakra
+    'no-restricted-imports': 'off',
+    '@typescript-eslint/no-restricted-imports': [
+      'warn',
+      {
+        paths: [
+          {
+            name: '@chakra-ui/react',
+            message: "Please import from '@invoke-ai/ui' instead.",
+          },
+          {
+            name: '@chakra-ui/layout',
+            message: "Please import from '@invoke-ai/ui' instead.",
+          },
+          {
+            name: '@chakra-ui/portal',
+            message: "Please import from '@invoke-ai/ui' instead.",
+          },
+        ],
+      },
+    ],
   },
   overrides: [
     {

invokeai/frontend/web/.storybook/ReduxInit.tsx

@@ -1,7 +1,7 @@
 import { PropsWithChildren, memo, useEffect } from 'react';
 import { modelChanged } from '../src/features/parameters/store/generationSlice';
 import { useAppDispatch } from '../src/app/store/storeHooks';
-import { useGlobalModifiersInit } from '../src/common/hooks/useGlobalModifiers';
+import { useGlobalModifiersInit } from '@invoke-ai/ui';
 /**
  * Initializes some state for storybook. Must be in a different component
  * so that it is run inside the redux context.

invokeai/frontend/web/package.json

@@ -20,6 +20,7 @@
   ],
   "scripts": {
     "dev": "concurrently \"vite dev\" \"pnpm run theme:watch\"",
+    "hi": "echo test",
     "dev:host": "concurrently \"vite dev --host\" \"pnpm run theme:watch\"",
     "build": "pnpm run lint && vite build",
     "typegen": "node scripts/typegen.js",
@@ -32,8 +33,8 @@
     "fix": "eslint --fix . && prettier --log-level warn --write .",
     "preinstall": "npx only-allow pnpm",
     "postinstall": "pnpm run theme",
-    "theme": "chakra-cli tokens src/theme/theme.ts",
-    "theme:watch": "chakra-cli tokens src/theme/theme.ts --watch",
+    "theme": "chakra-cli tokens node_modules/@invoke-ai/ui",
+    "theme:watch": "chakra-cli tokens node_modules/@invoke-ai/ui --watch",
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
     "unimported": "npx unimported"
@@ -66,6 +67,7 @@
     "@emotion/react": "^11.11.3",
     "@emotion/styled": "^11.11.0",
     "@fontsource-variable/inter": "^5.0.16",
+    "@invoke-ai/ui": "0.0.10",
     "@mantine/form": "6.0.21",
     "@nanostores/react": "^0.7.1",
     "@reduxjs/toolkit": "2.0.1",
@@ -73,12 +75,12 @@
     "chakra-react-select": "^4.7.6",
     "compare-versions": "^6.1.0",
     "dateformat": "^5.0.3",
-    "framer-motion": "^10.17.9",
+    "framer-motion": "^10.18.0",
     "i18next": "^23.7.16",
     "i18next-http-backend": "^2.4.2",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
-    "konva": "^9.3.0",
+    "konva": "^9.3.1",
     "lodash-es": "^4.17.21",
     "nanostores": "^0.9.5",
     "new-github-issue-url": "^1.0.0",
@@ -90,29 +92,29 @@
     "react-dom": "^18.2.0",
     "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.12",
-    "react-hook-form": "^7.49.2",
-    "react-hotkeys-hook": "4.4.3",
+    "react-hook-form": "^7.49.3",
+    "react-hotkeys-hook": "4.4.4",
     "react-i18next": "^14.0.0",
-    "react-icons": "^4.12.0",
+    "react-icons": "^5.0.1",
     "react-konva": "^18.2.10",
-    "react-redux": "9.0.4",
-    "react-resizable-panels": "^1.0.8",
+    "react-redux": "9.1.0",
+    "react-resizable-panels": "^1.0.9",
     "react-select": "5.8.0",
     "react-textarea-autosize": "^8.5.3",
-    "react-use": "^17.4.2",
+    "react-use": "^17.4.3",
     "react-virtuoso": "^4.6.2",
-    "reactflow": "^11.10.1",
+    "reactflow": "^11.10.2",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
     "roarr": "^7.21.0",
     "serialize-error": "^11.0.3",
-    "socket.io-client": "^4.7.3",
+    "socket.io-client": "^4.7.4",
     "type-fest": "^4.9.0",
     "use-debounce": "^10.0.0",
     "use-image": "^1.1.1",
     "uuid": "^9.0.1",
     "zod": "^3.22.4",
-    "zod-validation-error": "^2.1.0"
+    "zod-validation-error": "^3.0.0"
   },
   "peerDependencies": {
     "@chakra-ui/cli": "^2.4.1",
@@ -124,32 +126,32 @@
   "devDependencies": {
     "@arthurgeron/eslint-plugin-react-usememo": "^2.2.3",
     "@chakra-ui/cli": "^2.4.1",
-    "@storybook/addon-docs": "^7.6.7",
-    "@storybook/addon-essentials": "^7.6.7",
-    "@storybook/addon-interactions": "^7.6.7",
-    "@storybook/addon-links": "^7.6.7",
-    "@storybook/addon-storysource": "^7.6.7",
-    "@storybook/blocks": "^7.6.7",
-    "@storybook/manager-api": "^7.6.7",
-    "@storybook/react": "^7.6.7",
-    "@storybook/react-vite": "^7.6.7",
-    "@storybook/test": "^7.6.7",
-    "@storybook/theming": "^7.6.7",
+    "@storybook/addon-docs": "^7.6.10",
+    "@storybook/addon-essentials": "^7.6.10",
+    "@storybook/addon-interactions": "^7.6.10",
+    "@storybook/addon-links": "^7.6.10",
+    "@storybook/addon-storysource": "^7.6.10",
+    "@storybook/blocks": "^7.6.10",
+    "@storybook/manager-api": "^7.6.10",
+    "@storybook/react": "^7.6.10",
+    "@storybook/react-vite": "^7.6.10",
+    "@storybook/test": "^7.6.10",
+    "@storybook/theming": "^7.6.10",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.10.7",
-    "@types/react": "^18.2.47",
+    "@types/node": "^20.11.5",
+    "@types/react": "^18.2.48",
     "@types/react-dom": "^18.2.18",
     "@types/uuid": "^9.0.7",
-    "@typescript-eslint/eslint-plugin": "^6.18.0",
-    "@typescript-eslint/parser": "^6.18.0",
+    "@typescript-eslint/eslint-plugin": "^6.19.0",
+    "@typescript-eslint/parser": "^6.19.0",
     "@vitejs/plugin-react-swc": "^3.5.0",
     "concurrently": "^8.2.2",
     "eslint": "^8.56.0",
     "eslint-config-prettier": "^9.1.0",
     "eslint-plugin-i18next": "^6.0.3",
     "eslint-plugin-import": "^2.29.1",
-    "eslint-plugin-path": "^1.2.3",
+    "eslint-plugin-path": "^1.2.4",
     "eslint-plugin-react": "^7.33.2",
     "eslint-plugin-react-hooks": "^4.6.0",
     "eslint-plugin-simple-import-sort": "^10.0.0",
@@ -158,16 +160,16 @@
     "madge": "^6.1.0",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^6.7.3",
-    "prettier": "^3.1.1",
+    "prettier": "^3.2.4",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^7.6.7",
+    "storybook": "^7.6.10",
     "ts-toolbelt": "^9.6.0",
     "typescript": "^5.3.3",
     "vite": "^5.0.11",
     "vite-plugin-css-injected-by-js": "^3.3.1",
-    "vite-plugin-dts": "^3.7.0",
+    "vite-plugin-dts": "^3.7.1",
     "vite-plugin-eslint": "^1.8.1",
-    "vite-tsconfig-paths": "^4.2.3"
+    "vite-tsconfig-paths": "^4.3.1"
   },
   "pnpm": {
     "patchedDependencies": {

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

@@ -1,5 +1,6 @@
 {
     "accessibility": {
+        "about": "About",
         "copyMetadataJson": "Copy metadata JSON",
         "createIssue": "Create Issue",
         "exitViewer": "Exit Viewer",
@@ -74,6 +75,8 @@
         }
     },
     "common": {
+        "aboutDesc": "Using Invoke for work? Check out:",
+        "aboutHeading": "Own Your Creative Power",
         "accept": "Accept",
         "advanced": "Advanced",
         "advancedOptions": "Advanced Options",
@@ -136,6 +139,7 @@
         "load": "Load",
         "loading": "Loading",
         "loadingInvokeAI": "Loading Invoke AI",
+        "localSystem": "Local System",
         "learnMore": "Learn More",
         "modelManager": "Model Manager",
         "nodeEditor": "Node Editor",
@@ -199,7 +203,11 @@
         "prevPage": "Previous Page",
         "nextPage": "Next Page",
         "unknownError": "Unknown Error",
-        "unsaved": "Unsaved"
+        "unsaved": "Unsaved",
+        "red": "Red",
+        "green": "Green",
+        "blue": "Blue",
+        "alpha": "Alpha"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -699,6 +707,7 @@
         "clearCheckpointFolder": "Clear Checkpoint Folder",
         "closeAdvanced": "Close Advanced",
         "config": "Config",
+        "configFile": "Config File",
         "configValidationMsg": "Path to the config file of your model.",
         "conversionNotSupported": "Conversion Not Supported",
         "convert": "Convert",
@@ -1089,6 +1098,7 @@
         "boundingBoxHeader": "Bounding Box",
         "boundingBoxHeight": "Bounding Box Height",
         "boundingBoxWidth": "Bounding Box Width",
+        "boxBlur": "Box Blur",
         "cancel": {
             "cancel": "Cancel",
             "immediate": "Cancel immediately",
@@ -1116,6 +1126,7 @@
         "enableNoiseSettings": "Enable Noise Settings",
         "faceRestoration": "Face Restoration",
         "general": "General",
+        "gaussianBlur": "Gaussian Blur",
         "height": "Height",
         "hidePreview": "Hide Preview",
         "hiresOptim": "High Res Optimization",
@@ -1363,6 +1374,7 @@
         "promptNotSet": "Prompt Not Set",
         "promptNotSetDesc": "Could not find prompt for this image.",
         "promptSet": "Prompt Set",
+        "resetInitialImage": "Reset Initial Image",
         "seedNotSet": "Seed Not Set",
         "seedNotSetDesc": "Could not find seed for this image.",
         "seedSet": "Seed Set",
@@ -1379,6 +1391,7 @@
         "uploadFailed": "Upload failed",
         "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "uploadFailedUnableToLoadDesc": "Unable to load file",
+        "uploadInitialImage": "Upload Initial Image",
         "upscalingFailed": "Upscaling Failed",
         "workflowLoaded": "Workflow Loaded",
         "problemRetrievingWorkflow": "Problem Retrieving Workflow",

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

@@ -546,7 +546,7 @@
         "upscaleImage": "Amplia Immagine",
         "scale": "Scala",
         "otherOptions": "Altre opzioni",
-        "seamlessTiling": "Piastrella senza cuciture",
+        "seamlessTiling": "Piastrella senza giunte",
         "hiresOptim": "Ottimizzazione alta risoluzione",
         "imageFit": "Adatta l'immagine iniziale alle dimensioni di output",
         "codeformerFidelity": "Fedeltà",
@@ -592,8 +592,8 @@
         "hidePreview": "Nascondi l'anteprima",
         "showPreview": "Mostra l'anteprima",
         "noiseSettings": "Rumore",
-        "seamlessXAxis": "Piastrella senza cucitura Asse X",
-        "seamlessYAxis": "Piastrella senza cucitura Asse Y",
+        "seamlessXAxis": "Piastrella senza giunte Asse X",
+        "seamlessYAxis": "Piastrella senza giunte Asse Y",
         "scheduler": "Campionatore",
         "boundingBoxWidth": "Larghezza riquadro di delimitazione",
         "boundingBoxHeight": "Altezza riquadro di delimitazione",
@@ -800,7 +800,7 @@
         "feature": {
             "prompt": "Questo è il campo del prompt. Il prompt include oggetti di generazione e termini stilistici. Puoi anche aggiungere il peso (importanza del token) nel prompt, ma i comandi e i parametri dell'interfaccia a linea di comando non funzioneranno.",
             "gallery": "Galleria visualizza le generazioni dalla cartella degli output man mano che vengono create. Le impostazioni sono memorizzate all'interno di file e accessibili dal menu contestuale.",
-            "other": "Queste opzioni abiliteranno modalità di elaborazione alternative per Invoke. 'Piastrella senza cuciture' creerà modelli ripetuti nell'output. 'Ottimizzazione Alta risoluzione' è la generazione in due passaggi con 'Immagine a Immagine': usa questa impostazione quando vuoi un'immagine più grande e più coerente senza artefatti. Ci vorrà più tempo del solito 'Testo a Immagine'.",
+            "other": "Queste opzioni abiliteranno modalità di elaborazione alternative per Invoke. 'Piastrella senza giunte' creerà immagini piastrellabili senza giunture. 'Ottimizzazione Alta risoluzione' è la generazione in due passaggi con 'Immagine a Immagine': usa questa impostazione quando vuoi un'immagine più grande e più coerente senza artefatti. Ci vorrà più tempo del solito 'Testo a Immagine'.",
             "seed": "Il valore del Seme influenza il rumore iniziale da cui è formata l'immagine. Puoi usare i semi già esistenti dalle immagini precedenti. 'Soglia del rumore' viene utilizzato per mitigare gli artefatti a valori CFG elevati (provare l'intervallo 0-10) e Perlin per aggiungere il rumore Perlin durante la generazione: entrambi servono per aggiungere variazioni ai risultati.",
             "variations": "Prova una variazione con un valore compreso tra 0.1 e 1.0 per modificare il risultato per un dato seme. Variazioni interessanti del seme sono comprese tra 0.1 e 0.3.",
             "upscale": "Utilizza ESRGAN per ingrandire l'immagine subito dopo la generazione.",
@@ -1171,7 +1171,7 @@
         "depthMidas": "Profondità (Midas)",
         "enableControlnet": "Abilita ControlNet",
         "detectResolution": "Rileva risoluzione",
-        "controlMode": "Modalità Controllo",
+        "controlMode": "Controllo",
         "cannyDescription": "Canny rilevamento bordi",
         "depthZoe": "Profondità (Zoe)",
         "autoConfigure": "Configura automaticamente il processore",
@@ -1187,13 +1187,13 @@
         "ipAdapterModel": "Modello Adattatore",
         "resetControlImage": "Reimposta immagine di controllo",
         "f": "F",
-        "h": "H",
+        "h": "A",
         "prompt": "Prompt",
         "openPoseDescription": "Stima della posa umana utilizzando Openpose",
-        "resizeMode": "Modalità ridimensionamento",
+        "resizeMode": "Ridimensionamento",
         "weight": "Peso",
         "selectModel": "Seleziona un modello",
-        "w": "W",
+        "w": "L",
         "processor": "Processore",
         "none": "Nessuno",
         "pidiDescription": "Elaborazione immagini PIDI",
@@ -1215,7 +1215,7 @@
         "hedDescription": "Rilevamento dei bordi nidificati olisticamente",
         "setControlImageDimensions": "Imposta le dimensioni dell'immagine di controllo su L/A",
         "resetIPAdapterImage": "Reimposta immagine Adattatore IP",
-        "handAndFace": "Mano e faccia",
+        "handAndFace": "Mani e volti",
         "enableIPAdapter": "Abilita Adattatore IP",
         "maxFaces": "Numero massimo di volti",
         "addT2IAdapter": "Aggiungi $t(common.t2iAdapter)",
@@ -1229,7 +1229,7 @@
         "controlAdapter_other": "Adattatori di Controllo",
         "megaControl": "Mega ControlNet",
         "minConfidence": "Confidenza minima",
-        "scribble": "Scribble",
+        "scribble": "Scarabocchio",
         "amult": "Angolo di illuminazione",
         "coarse": "Approssimativo",
         "resizeSimple": "Ridimensiona (semplice)"

invokeai/frontend/web/src/app/components/App.tsx

@@ -1,4 +1,4 @@
-import { Box } from '@chakra-ui/react';
+import { Box, useGlobalModifiersInit } from '@invoke-ai/ui';
 import { useSocketIO } from 'app/hooks/useSocketIO';
 import { useLogger } from 'app/logging/useLogger';
 import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
@@ -8,7 +8,6 @@ import ImageUploadOverlay from 'common/components/ImageUploadOverlay';
 import { useClearStorage } from 'common/hooks/useClearStorage';
 import { useFullscreenDropzone } from 'common/hooks/useFullscreenDropzone';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
-import { useGlobalModifiersInit } from 'common/hooks/useGlobalModifiers';
 import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
 import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';

invokeai/frontend/web/src/app/components/AppErrorBoundaryFallback.tsx

@@ -1,11 +1,12 @@
-import { Flex, Heading, Link, useToast } from '@chakra-ui/react';
-import { InvButton } from 'common/components/InvButton/InvButton';
-import { InvText } from 'common/components/InvText/wrapper';
+import { Button, Flex, Heading, Link, Text, useToast } from '@invoke-ai/ui';
 import newGithubIssueUrl from 'new-github-issue-url';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { FaCopy, FaExternalLinkAlt } from 'react-icons/fa';
-import { FaArrowRotateLeft } from 'react-icons/fa6';
+import {
+  PiArrowCounterClockwiseBold,
+  PiArrowSquareOutBold,
+  PiCopyBold,
+} from 'react-icons/pi';
 import { serializeError } from 'serialize-error';
 
 type Props = {
@@ -62,24 +63,24 @@ const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
           justifyContent="space-between"
           alignItems="center"
         >
-          <InvText fontWeight="semibold" color="error.400">
+          <Text fontWeight="semibold" color="error.400">
             {error.name}: {error.message}
-          </InvText>
+          </Text>
         </Flex>
         <Flex gap={4}>
-          <InvButton
-            leftIcon={<FaArrowRotateLeft />}
+          <Button
+            leftIcon={<PiArrowCounterClockwiseBold />}
             onClick={resetErrorBoundary}
           >
             {t('accessibility.resetUI')}
-          </InvButton>
-          <InvButton leftIcon={<FaCopy />} onClick={handleCopy}>
+          </Button>
+          <Button leftIcon={<PiCopyBold />} onClick={handleCopy}>
             {t('common.copyError')}
-          </InvButton>
+          </Button>
           <Link href={url} isExternal>
-            <InvButton leftIcon={<FaExternalLinkAlt />}>
+            <Button leftIcon={<PiArrowSquareOutBold />}>
               {t('accessibility.createIssue')}
-            </InvButton>
+            </Button>
           </Link>
         </Flex>
       </Flex>

invokeai/frontend/web/src/app/components/ThemeLocaleProvider.tsx

@@ -1,12 +1,16 @@
 import '@fontsource-variable/inter';
 import 'overlayscrollbars/overlayscrollbars.css';
-import 'common/components/OverlayScrollbars/overlayscrollbars.css';
 
-import { ChakraProvider, extendTheme } from '@chakra-ui/react';
+import {
+  ChakraProvider,
+  DarkMode,
+  extendTheme,
+  theme as _theme,
+  TOAST_OPTIONS,
+} from '@invoke-ai/ui';
 import type { ReactNode } from 'react';
 import { memo, useEffect, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { theme as invokeAITheme, TOAST_OPTIONS } from 'theme/theme';
 
 type ThemeLocaleProviderProps = {
   children: ReactNode;
@@ -19,7 +23,7 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
 
   const theme = useMemo(() => {
     return extendTheme({
-      ...invokeAITheme,
+      ..._theme,
       direction,
     });
   }, [direction]);
@@ -30,7 +34,7 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
 
   return (
     <ChakraProvider theme={theme} toastOptions={TOAST_OPTIONS}>
-      {children}
+      <DarkMode>{children}</DarkMode>
     </ChakraProvider>
   );
 }

invokeai/frontend/web/src/app/components/Toaster.ts

@@ -1,4 +1,4 @@
-import { useToast } from '@chakra-ui/react';
+import { useToast } from '@invoke-ai/ui';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { addToast, clearToastQueue } from 'features/system/store/systemSlice';
 import type { MakeToastArg } from 'features/system/util/makeToast';

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

@@ -1,11 +1,10 @@
-import { createStandaloneToast } from '@chakra-ui/react';
+import { createStandaloneToast, theme, TOAST_OPTIONS } from '@invoke-ai/ui';
 import { logger } from 'app/logging/logger';
 import { parseify } from 'common/util/serialize';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
 import { t } from 'i18next';
 import { truncate, upperFirst } from 'lodash-es';
 import { queueApi } from 'services/api/endpoints/queue';
-import { theme, TOAST_OPTIONS } from 'theme/theme';
 
 import { startAppListening } from '..';
 

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

@@ -1,4 +1,4 @@
-import type { UseToastOptions } from '@chakra-ui/react';
+import type { UseToastOptions } from '@invoke-ai/ui';
 import { logger } from 'app/logging/logger';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 import {

invokeai/frontend/web/src/app/store/nanostores/customStarUI.ts

@@ -1,4 +1,4 @@
-import type { MenuItemProps } from '@chakra-ui/react';
+import type { MenuItemProps } from '@invoke-ai/ui';
 import { atom } from 'nanostores';
 
 export type CustomStarUi = {

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

@@ -1,5 +1,10 @@
-import type { ChakraProps } from '@chakra-ui/react';
-import { Flex } from '@chakra-ui/react';
+import type { ChakraProps } from '@invoke-ai/ui';
+import {
+  CompositeNumberInput,
+  Flex,
+  FormControl,
+  FormLabel,
+} from '@invoke-ai/ui';
 import type { CSSProperties } from 'react';
 import { memo, useCallback } from 'react';
 import { RgbaColorPicker } from 'react-colorful';
@@ -7,9 +12,7 @@ import type {
   ColorPickerBaseProps,
   RgbaColor,
 } from 'react-colorful/dist/types';
-
-import { InvControl } from './InvControl/InvControl';
-import { InvNumberInput } from './InvNumberInput/InvNumberInput';
+import { useTranslation } from 'react-i18next';
 
 type IAIColorPickerProps = ColorPickerBaseProps<RgbaColor> & {
   withNumberInput?: boolean;
@@ -35,6 +38,7 @@ const numberInputWidth: ChakraProps['w'] = '4.2rem';
 
 const IAIColorPicker = (props: IAIColorPickerProps) => {
   const { color, onChange, withNumberInput, ...rest } = props;
+  const { t } = useTranslation();
   const handleChangeR = useCallback(
     (r: number) => onChange({ ...color, r }),
     [color, onChange]
@@ -61,8 +65,9 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
       />
       {withNumberInput && (
         <Flex>
-          <InvControl label="Red">
-            <InvNumberInput
+          <FormControl>
+            <FormLabel>{t('common.red')}</FormLabel>
+            <CompositeNumberInput
               value={color.r}
               onChange={handleChangeR}
               min={0}
@@ -71,9 +76,10 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               w={numberInputWidth}
               defaultValue={90}
             />
-          </InvControl>
-          <InvControl label="Green">
-            <InvNumberInput
+          </FormControl>
+          <FormControl>
+            <FormLabel>{t('common.green')}</FormLabel>
+            <CompositeNumberInput
               value={color.g}
               onChange={handleChangeG}
               min={0}
@@ -82,9 +88,10 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               w={numberInputWidth}
               defaultValue={90}
             />
-          </InvControl>
-          <InvControl label="Blue">
-            <InvNumberInput
+          </FormControl>
+          <FormControl>
+            <FormLabel>{t('common.blue')}</FormLabel>
+            <CompositeNumberInput
               value={color.b}
               onChange={handleChangeB}
               min={0}
@@ -93,9 +100,10 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               w={numberInputWidth}
               defaultValue={255}
             />
-          </InvControl>
-          <InvControl label="Alpha">
-            <InvNumberInput
+          </FormControl>
+          <FormControl>
+            <FormLabel>{t('common.alpha')}</FormLabel>
+            <CompositeNumberInput
               value={color.a}
               onChange={handleChangeA}
               step={0.1}
@@ -104,7 +112,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               w={numberInputWidth}
               defaultValue={1}
             />
-          </InvControl>
+          </FormControl>
         </Flex>
       )}
     </Flex>

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

@@ -1,9 +1,5 @@
-import type {
-  ChakraProps,
-  FlexProps,
-  SystemStyleObject,
-} from '@chakra-ui/react';
-import { Flex, Icon, Image } from '@chakra-ui/react';
+import type { ChakraProps, FlexProps, SystemStyleObject } from '@invoke-ai/ui';
+import { Flex, Icon, Image } from '@invoke-ai/ui';
 import {
   IAILoadingImageFallback,
   IAINoContentFallback,
@@ -22,16 +18,16 @@ import type {
   SyntheticEvent,
 } from 'react';
 import { memo, useCallback, useMemo, useState } from 'react';
-import { FaImage, FaUpload } from 'react-icons/fa';
+import { PiImageBold, PiUploadSimpleBold } from 'react-icons/pi';
 import type { ImageDTO, PostUploadAction } from 'services/api/types';
 
 import IAIDraggable from './IAIDraggable';
 import IAIDroppable from './IAIDroppable';
 import SelectionOverlay from './SelectionOverlay';
 
-const defaultUploadElement = <Icon as={FaUpload} boxSize={16} />;
+const defaultUploadElement = <Icon as={PiUploadSimpleBold} boxSize={16} />;
 
-const defaultNoContentFallback = <IAINoContentFallback icon={FaImage} />;
+const defaultNoContentFallback = <IAINoContentFallback icon={PiImageBold} />;
 
 type IAIDndImageProps = FlexProps & {
   imageDTO: ImageDTO | undefined;

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

@@ -1,9 +1,8 @@
-import type { SystemStyleObject } from '@chakra-ui/react';
+import type { SystemStyleObject } from '@invoke-ai/ui';
+import { IconButton } from '@invoke-ai/ui';
 import type { MouseEvent, ReactElement } from 'react';
 import { memo, useMemo } from 'react';
 
-import { InvIconButton } from './InvIconButton/InvIconButton';
-
 type Props = {
   onClick: (event: MouseEvent<HTMLButtonElement>) => void;
   tooltip: string;
@@ -26,7 +25,7 @@ const IAIDndImageIcon = (props: Props) => {
         transitionDuration: 'normal',
         fill: 'base.100',
         _hover: { fill: 'base.50' },
-        filter: 'drop-shadow(0px 0px 0.1rem var(--invokeai-colors-base-800))',
+        filter: 'drop-shadow(0px 0px 0.1rem var(--invoke-colors-base-800))',
       },
       ...styleOverrides,
     }),
@@ -34,7 +33,7 @@ const IAIDndImageIcon = (props: Props) => {
   );
 
   return (
-    <InvIconButton
+    <IconButton
       onClick={onClick}
       aria-label={tooltip}
       tooltip={tooltip}

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

@@ -1,5 +1,5 @@
-import type { BoxProps } from '@chakra-ui/react';
-import { Box } from '@chakra-ui/react';
+import type { BoxProps } from '@invoke-ai/ui';
+import { Box } from '@invoke-ai/ui';
 import { useDraggableTypesafe } from 'features/dnd/hooks/typesafeHooks';
 import type { TypesafeDraggableData } from 'features/dnd/types';
 import { memo, useRef } from 'react';

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

@@ -1,10 +1,10 @@
-import { Box, Flex } from '@chakra-ui/react';
+import { Box, Flex } from '@invoke-ai/ui';
 import type { AnimationProps } from 'framer-motion';
 import { motion } from 'framer-motion';
 import type { ReactNode } from 'react';
 import { memo, useRef } from 'react';
+import { useTranslation } from 'react-i18next';
 import { v4 as uuidv4 } from 'uuid';
-
 type Props = {
   isOver: boolean;
   label?: ReactNode;
@@ -23,7 +23,8 @@ const exit: AnimationProps['exit'] = {
 };
 
 const IAIDropOverlay = (props: Props) => {
-  const { isOver, label = 'Drop' } = props;
+  const { t } = useTranslation();
+  const { isOver, label = t('gallery.drop') } = props;
   const motionId = useRef(uuidv4());
   return (
     <motion.div

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

@@ -1,4 +1,4 @@
-import { Box } from '@chakra-ui/react';
+import { Box } from '@invoke-ai/ui';
 import { useDroppableTypesafe } from 'features/dnd/hooks/typesafeHooks';
 import type { TypesafeDroppableData } from 'features/dnd/types';
 import { isValidDrop } from 'features/dnd/util/isValidDrop';

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

@@ -1,5 +1,5 @@
-import type { SystemStyleObject } from '@chakra-ui/react';
-import { Box, Skeleton } from '@chakra-ui/react';
+import type { SystemStyleObject } from '@invoke-ai/ui';
+import { Box, Skeleton } from '@invoke-ai/ui';
 import { memo } from 'react';
 
 const skeletonStyles: SystemStyleObject = {

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

@@ -1,11 +1,9 @@
-import type { As, FlexProps, StyleProps } from '@chakra-ui/react';
-import { Flex, Icon, Skeleton, Spinner } from '@chakra-ui/react';
+import type { As, ChakraProps, FlexProps } from '@invoke-ai/ui';
+import { Flex, Icon, Skeleton, Spinner, Text } from '@invoke-ai/ui';
 import { memo, useMemo } from 'react';
-import { FaImage } from 'react-icons/fa';
+import { PiImageBold } from 'react-icons/pi';
 import type { ImageDTO } from 'services/api/types';
 
-import { InvText } from './InvText/wrapper';
-
 type Props = { image: ImageDTO | undefined };
 
 export const IAILoadingImageFallback = memo((props: Props) => {
@@ -39,11 +37,11 @@ IAILoadingImageFallback.displayName = 'IAILoadingImageFallback';
 type IAINoImageFallbackProps = FlexProps & {
   label?: string;
   icon?: As | null;
-  boxSize?: StyleProps['boxSize'];
+  boxSize?: ChakraProps['boxSize'];
 };
 
 export const IAINoContentFallback = memo((props: IAINoImageFallbackProps) => {
-  const { icon = FaImage, boxSize = 16, sx, ...rest } = props;
+  const { icon = PiImageBold, boxSize = 16, sx, ...rest } = props;
 
   const styles = useMemo(
     () => ({
@@ -66,9 +64,9 @@ export const IAINoContentFallback = memo((props: IAINoImageFallbackProps) => {
     <Flex sx={styles} {...rest}>
       {icon && <Icon as={icon} boxSize={boxSize} opacity={0.7} />}
       {props.label && (
-        <InvText textAlign="center" fontSize="md">
+        <Text textAlign="center" fontSize="md">
           {props.label}
-        </InvText>
+        </Text>
       )}
     </Flex>
   );
@@ -102,7 +100,7 @@ export const IAINoContentFallbackWithSpinner = memo(
     return (
       <Flex sx={styles} {...rest}>
         <Spinner size="xl" />
-        {props.label && <InvText textAlign="center">{props.label}</InvText>}
+        {props.label && <Text textAlign="center">{props.label}</Text>}
       </Flex>
     );
   }

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

@@ -1,147 +0,0 @@
-import { Divider, Flex, Image, Portal } from '@chakra-ui/react';
-import { useAppSelector } from 'app/store/storeHooks';
-import { InvButton } from 'common/components/InvButton/InvButton';
-import { InvHeading } from 'common/components/InvHeading/wrapper';
-import {
-  InvPopover,
-  InvPopoverBody,
-  InvPopoverCloseButton,
-  InvPopoverContent,
-  InvPopoverTrigger,
-} from 'common/components/InvPopover/wrapper';
-import { InvText } from 'common/components/InvText/wrapper';
-import { merge, omit } from 'lodash-es';
-import type { ReactElement } from 'react';
-import { memo, useCallback, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { FaExternalLinkAlt } from 'react-icons/fa';
-
-import type { Feature, PopoverData } from './constants';
-import { OPEN_DELAY, POPOVER_DATA, POPPER_MODIFIERS } from './constants';
-
-type Props = {
-  feature: Feature;
-  inPortal?: boolean;
-  children: ReactElement;
-};
-
-const IAIInformationalPopover = ({
-  feature,
-  children,
-  inPortal = true,
-  ...rest
-}: Props) => {
-  const shouldEnableInformationalPopovers = useAppSelector(
-    (s) => s.system.shouldEnableInformationalPopovers
-  );
-
-  const data = useMemo(() => POPOVER_DATA[feature], [feature]);
-
-  const popoverProps = useMemo(
-    () => merge(omit(data, ['image', 'href', 'buttonLabel']), rest),
-    [data, rest]
-  );
-
-  if (!shouldEnableInformationalPopovers) {
-    return children;
-  }
-
-  return (
-    <InvPopover
-      isLazy
-      closeOnBlur={false}
-      trigger="hover"
-      variant="informational"
-      openDelay={OPEN_DELAY}
-      modifiers={POPPER_MODIFIERS}
-      placement="top"
-      {...popoverProps}
-    >
-      <InvPopoverTrigger>{children}</InvPopoverTrigger>
-      {inPortal ? (
-        <Portal>
-          <PopoverContent data={data} feature={feature} />
-        </Portal>
-      ) : (
-        <PopoverContent data={data} feature={feature} />
-      )}
-    </InvPopover>
-  );
-};
-
-export default memo(IAIInformationalPopover);
-
-type PopoverContentProps = {
-  data?: PopoverData;
-  feature: Feature;
-};
-
-const PopoverContent = ({ data, feature }: PopoverContentProps) => {
-  const { t } = useTranslation();
-
-  const heading = useMemo<string | undefined>(
-    () => t(`popovers.${feature}.heading`),
-    [feature, t]
-  );
-
-  const paragraphs = useMemo<string[]>(
-    () =>
-      t(`popovers.${feature}.paragraphs`, {
-        returnObjects: true,
-      }) ?? [],
-    [feature, t]
-  );
-
-  const handleClick = useCallback(() => {
-    if (!data?.href) {
-      return;
-    }
-    window.open(data.href);
-  }, [data?.href]);
-
-  return (
-    <InvPopoverContent w={96}>
-      <InvPopoverCloseButton />
-      <InvPopoverBody>
-        <Flex gap={2} flexDirection="column" alignItems="flex-start">
-          {heading && (
-            <>
-              <InvHeading size="sm">{heading}</InvHeading>
-              <Divider />
-            </>
-          )}
-          {data?.image && (
-            <>
-              <Image
-                objectFit="contain"
-                maxW="60%"
-                maxH="60%"
-                backgroundColor="white"
-                src={data.image}
-                alt="Optional Image"
-              />
-              <Divider />
-            </>
-          )}
-          {paragraphs.map((p) => (
-            <InvText key={p}>{p}</InvText>
-          ))}
-          {data?.href && (
-            <>
-              <Divider />
-              <InvButton
-                pt={1}
-                onClick={handleClick}
-                leftIcon={<FaExternalLinkAlt />}
-                alignSelf="flex-end"
-                variant="link"
-              >
-                {t('common.learnMore') ?? heading}
-              </InvButton>
-            </>
-          )}
-        </Flex>
-      </InvPopoverBody>
-    </InvPopoverContent>
-  );
-};

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

@@ -1,4 +1,4 @@
-import { Badge, Flex } from '@chakra-ui/react';
+import { Badge, Flex } from '@invoke-ai/ui';
 import { memo } from 'react';
 import type { ImageDTO } from 'services/api/types';
 

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

@@ -1,4 +1,4 @@
-import { Box, Flex, Heading } from '@chakra-ui/react';
+import { Box, Flex, Heading } from '@invoke-ai/ui';
 import type { AnimationProps } from 'framer-motion';
 import { motion } from 'framer-motion';
 import { memo } from 'react';

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

@@ -0,0 +1,147 @@
+import {
+  Button,
+  Divider,
+  Flex,
+  Heading,
+  Image,
+  Popover,
+  PopoverBody,
+  PopoverCloseButton,
+  PopoverContent,
+  PopoverTrigger,
+  Portal,
+  Text,
+} from '@invoke-ai/ui';
+import { useAppSelector } from 'app/store/storeHooks';
+import { merge, omit } from 'lodash-es';
+import type { ReactElement } from 'react';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiArrowSquareOutBold } from 'react-icons/pi';
+
+import type { Feature, PopoverData } from './constants';
+import { OPEN_DELAY, POPOVER_DATA, POPPER_MODIFIERS } from './constants';
+
+type Props = {
+  feature: Feature;
+  inPortal?: boolean;
+  children: ReactElement;
+};
+
+export const InformationalPopover = memo(
+  ({ feature, children, inPortal = true, ...rest }: Props) => {
+    const shouldEnableInformationalPopovers = useAppSelector(
+      (s) => s.system.shouldEnableInformationalPopovers
+    );
+
+    const data = useMemo(() => POPOVER_DATA[feature], [feature]);
+
+    const popoverProps = useMemo(
+      () => merge(omit(data, ['image', 'href', 'buttonLabel']), rest),
+      [data, rest]
+    );
+
+    if (!shouldEnableInformationalPopovers) {
+      return children;
+    }
+
+    return (
+      <Popover
+        isLazy
+        closeOnBlur={false}
+        trigger="hover"
+        variant="informational"
+        openDelay={OPEN_DELAY}
+        modifiers={POPPER_MODIFIERS}
+        placement="top"
+        {...popoverProps}
+      >
+        <PopoverTrigger>{children}</PopoverTrigger>
+        {inPortal ? (
+          <Portal>
+            <Content data={data} feature={feature} />
+          </Portal>
+        ) : (
+          <Content data={data} feature={feature} />
+        )}
+      </Popover>
+    );
+  }
+);
+
+InformationalPopover.displayName = 'InformationalPopover';
+
+type ContentProps = {
+  data?: PopoverData;
+  feature: Feature;
+};
+
+const Content = ({ data, feature }: ContentProps) => {
+  const { t } = useTranslation();
+
+  const heading = useMemo<string | undefined>(
+    () => t(`popovers.${feature}.heading`),
+    [feature, t]
+  );
+
+  const paragraphs = useMemo<string[]>(
+    () =>
+      t(`popovers.${feature}.paragraphs`, {
+        returnObjects: true,
+      }) ?? [],
+    [feature, t]
+  );
+
+  const handleClick = useCallback(() => {
+    if (!data?.href) {
+      return;
+    }
+    window.open(data.href);
+  }, [data?.href]);
+
+  return (
+    <PopoverContent w={96}>
+      <PopoverCloseButton />
+      <PopoverBody>
+        <Flex gap={2} flexDirection="column" alignItems="flex-start">
+          {heading && (
+            <>
+              <Heading size="sm">{heading}</Heading>
+              <Divider />
+            </>
+          )}
+          {data?.image && (
+            <>
+              <Image
+                objectFit="contain"
+                maxW="60%"
+                maxH="60%"
+                backgroundColor="white"
+                src={data.image}
+                alt="Optional Image"
+              />
+              <Divider />
+            </>
+          )}
+          {paragraphs.map((p) => (
+            <Text key={p}>{p}</Text>
+          ))}
+          {data?.href && (
+            <>
+              <Divider />
+              <Button
+                pt={1}
+                onClick={handleClick}
+                leftIcon={<PiArrowSquareOutBold />}
+                alignSelf="flex-end"
+                variant="link"
+              >
+                {t('common.learnMore') ?? heading}
+              </Button>
+            </>
+          )}
+        </Flex>
+      </PopoverBody>
+    </PopoverContent>
+  );
+};

invokeai/frontend/web/src/common/components/InformationalPopover/constants.ts

@@ -1,4 +1,4 @@
-import type { PopoverProps } from '@chakra-ui/react';
+import type { PopoverProps } from '@invoke-ai/ui';
 
 export type Feature =
   | 'clipSkip'

invokeai/frontend/web/src/common/components/InvAccordion/InvAccordion.stories.tsx

@@ -1,70 +0,0 @@
-import type { Meta, StoryObj } from '@storybook/react';
-import { InvText } from 'common/components/InvText/wrapper';
-
-import { InvAccordionButton } from './InvAccordionButton';
-import type { InvAccordionProps } from './types';
-import { InvAccordion, InvAccordionItem, InvAccordionPanel } from './wrapper';
-
-const meta: Meta<typeof InvAccordion> = {
-  title: 'Primitives/InvAccordion',
-  tags: ['autodocs'],
-  component: InvAccordion,
-  args: {
-    colorScheme: 'base',
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof InvAccordion>;
-
-const Component = (props: InvAccordionProps) => {
-  return (
-    <InvAccordion {...props} defaultIndex={[0]} allowMultiple>
-      <InvAccordionItem>
-        <InvAccordionButton badges={['and', 'i', 'said']}>
-          Section 1 title
-        </InvAccordionButton>
-        <InvAccordionPanel p={4}>
-          <InvText>
-            25 years and my life is still Tryin&apos; to get up that great big
-            hill of hope For a destination I realized quickly when I knew I
-            should That the world was made up of this brotherhood of man For
-            whatever that means
-          </InvText>
-        </InvAccordionPanel>
-      </InvAccordionItem>
-
-      <InvAccordionItem>
-        <InvAccordionButton badges={['heeeyyyyyy']}>
-          Section 1 title
-        </InvAccordionButton>
-        <InvAccordionPanel p={4}>
-          <InvText>
-            And so I cry sometimes when I&apos;m lying in bed Just to get it all
-            out what&apos;s in my head And I, I am feeling a little peculiar And
-            so I wake in the morning and I step outside And I take a deep breath
-            and I get real high And I scream from the top of my lungs
-            &quot;What&apos;s going on?&quot;
-          </InvText>
-        </InvAccordionPanel>
-      </InvAccordionItem>
-
-      <InvAccordionItem>
-        <InvAccordionButton badges={["what's", 'goin', 'on', '?']}>
-          Section 2 title
-        </InvAccordionButton>
-        <InvAccordionPanel p={4}>
-          <InvText>
-            And I say, hey-ey-ey Hey-ey-ey I said &quot;Hey, a-what&apos;s going
-            on?&quot; And I say, hey-ey-ey Hey-ey-ey I said &quot;Hey,
-            a-what&apos;s going on?&quot;
-          </InvText>
-        </InvAccordionPanel>
-      </InvAccordionItem>
-    </InvAccordion>
-  );
-};
-
-export const Default: Story = {
-  render: Component,
-};

invokeai/frontend/web/src/common/components/InvAccordion/InvAccordionButton.tsx

@@ -1,31 +0,0 @@
-import {
-  AccordionButton as ChakraAccordionButton,
-  Spacer,
-} from '@chakra-ui/react';
-import { InvBadge } from 'common/components/InvBadge/wrapper';
-import { truncate } from 'lodash-es';
-import { useMemo } from 'react';
-
-import type { InvAccordionButtonProps } from './types';
-import { InvAccordionIcon } from './wrapper';
-
-export const InvAccordionButton = (props: InvAccordionButtonProps) => {
-  const { children, badges: _badges, ...rest } = props;
-  const badges = useMemo<string[] | undefined>(
-    () =>
-      _badges?.map((b) => truncate(String(b), { length: 24, omission: '...' })),
-    [_badges]
-  );
-  return (
-    <ChakraAccordionButton {...rest}>
-      {children}
-      <Spacer />
-      {badges?.map((b, i) => (
-        <InvBadge key={`${b}.${i}`} colorScheme="invokeBlue">
-          {b}
-        </InvBadge>
-      ))}
-      <InvAccordionIcon />
-    </ChakraAccordionButton>
-  );
-};

invokeai/frontend/web/src/common/components/InvAccordion/theme.ts

@@ -1,71 +0,0 @@
-import { accordionAnatomy as parts } from '@chakra-ui/anatomy';
-import {
-  createMultiStyleConfigHelpers,
-  defineStyle,
-} from '@chakra-ui/styled-system';
-
-const { definePartsStyle, defineMultiStyleConfig } =
-  createMultiStyleConfigHelpers(parts.keys);
-
-const invokeAIContainer = defineStyle({
-  border: 'none',
-  bg: 'base.850',
-  borderRadius: 'base',
-  ':has(&div &button:hover)': { bg: 'base.800' },
-  transitionProperty: 'common',
-  transitionDuration: '0.1s',
-});
-
-const invokeAIButton = defineStyle((_props) => {
-  return {
-    gap: 2,
-    fontWeight: 'semibold',
-    fontSize: 'sm',
-    border: 'none',
-    borderRadius: 'base',
-    color: 'base.300',
-    _hover: {},
-    _expanded: {
-      borderBottomRadius: 'none',
-    },
-  };
-});
-
-const invokeAIPanel = defineStyle((props) => {
-  const { colorScheme: c } = props;
-  return {
-    bg: `${c}.800`,
-    borderRadius: 'base',
-    p: 0,
-    transitionProperty: 'common',
-    transitionDuration: '0.1s',
-  };
-});
-
-const invokeAIIcon = defineStyle({
-  ms: 2,
-});
-
-const invokeAI = definePartsStyle((props) => ({
-  container: invokeAIContainer,
-  button: invokeAIButton(props),
-  panel: invokeAIPanel(props),
-  icon: invokeAIIcon,
-}));
-
-const baseStyle = definePartsStyle(() => ({
-  root: {
-    display: 'flex',
-    flexDirection: 'column',
-    gap: 4,
-  },
-}));
-
-export const accordionTheme = defineMultiStyleConfig({
-  baseStyle,
-  variants: { invokeAI },
-  defaultProps: {
-    variant: 'invokeAI',
-    colorScheme: 'base',
-  },
-});

invokeai/frontend/web/src/common/components/InvAccordion/types.ts

@@ -1,11 +0,0 @@
-import type { AccordionButtonProps as ChakraAccordionButtonProps } from '@chakra-ui/react';
-export type {
-  AccordionIconProps as InvAccordionIconProps,
-  AccordionItemProps as InvAccordionItemProps,
-  AccordionPanelProps as InvAccordionPanelProps,
-  AccordionProps as InvAccordionProps,
-} from '@chakra-ui/react';
-
-export type InvAccordionButtonProps = ChakraAccordionButtonProps & {
-  badges?: (string | number)[];
-};

invokeai/frontend/web/src/common/components/InvAccordion/wrapper.ts

@@ -1,6 +0,0 @@
-export {
-  Accordion as InvAccordion,
-  AccordionIcon as InvAccordionIcon,
-  AccordionItem as InvAccordionItem,
-  AccordionPanel as InvAccordionPanel,
-} from '@chakra-ui/react';

invokeai/frontend/web/src/common/components/InvAlertDialog/types.ts

@@ -1,4 +0,0 @@
-/**
- * AlertDialog is a chakra Modal internally and uses those props.
- */
-export type { AlertDialogProps as InvAlertDialogProps } from '@chakra-ui/react';

invokeai/frontend/web/src/common/components/InvAlertDialog/wrapper.ts

@@ -1,9 +0,0 @@
-export {
-  AlertDialog as InvAlertDialog,
-  AlertDialogBody as InvAlertDialogBody,
-  AlertDialogCloseButton as InvAlertDialogCloseButton,
-  AlertDialogContent as InvAlertDialogContent,
-  AlertDialogFooter as InvAlertDialogFooter,
-  AlertDialogHeader as InvAlertDialogHeader,
-  AlertDialogOverlay as InvAlertDialogOverlay,
-} from '@chakra-ui/react';

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

@@ -1,40 +0,0 @@
-import { Box, forwardRef, Textarea as ChakraTextarea } from '@chakra-ui/react';
-import { useGlobalModifiersSetters } from 'common/hooks/useGlobalModifiers';
-import { stopPastePropagation } from 'common/util/stopPastePropagation';
-import type { KeyboardEvent } from 'react';
-import { memo, useCallback } from 'react';
-import ResizeTextarea from 'react-textarea-autosize';
-
-import type { InvAutosizeTextareaProps } from './types';
-
-export const InvAutosizeTextarea = memo(
-  forwardRef<InvAutosizeTextareaProps, typeof ResizeTextarea>(
-    (props: InvAutosizeTextareaProps, ref) => {
-      const { setShift } = useGlobalModifiersSetters();
-      const onKeyUpDown = useCallback(
-        (e: KeyboardEvent<HTMLTextAreaElement>) => {
-          setShift(e.shiftKey);
-        },
-        [setShift]
-      );
-      return (
-        <Box pos="relative">
-          <ChakraTextarea
-            as={ResizeTextarea}
-            ref={ref}
-            overflow="scroll"
-            w="100%"
-            minRows={3}
-            minH={20}
-            onPaste={stopPastePropagation}
-            onKeyUp={onKeyUpDown}
-            onKeyDown={onKeyUpDown}
-            {...props}
-          />
-        </Box>
-      );
-    }
-  )
-);
-
-InvAutosizeTextarea.displayName = 'InvAutosizeTextarea';

invokeai/frontend/web/src/common/components/InvAutosizeTextarea/InvTextarea.stories.tsx

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

invokeai/frontend/web/src/common/components/InvAutosizeTextarea/types.ts

@@ -1,7 +0,0 @@
-import type { TextareaProps as ChakraTextareaProps } from '@chakra-ui/react';
-import type { TextareaAutosizeProps } from 'react-textarea-autosize';
-
-export type InvAutosizeTextareaProps = Omit<
-  ChakraTextareaProps & TextareaAutosizeProps,
-  'resize'
->;

invokeai/frontend/web/src/common/components/InvBadge/InvBadge.stories.tsx

@@ -1,24 +0,0 @@
-import type { Meta, StoryObj } from '@storybook/react';
-
-import type { InvBadgeProps } from './types';
-import { InvBadge } from './wrapper';
-
-const meta: Meta<typeof InvBadge> = {
-  title: 'Primitives/InvBadge',
-  tags: ['autodocs'],
-  component: InvBadge,
-  args: {
-    colorScheme: 'base',
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof InvBadge>;
-
-const Component = (props: InvBadgeProps) => {
-  return <InvBadge {...props}>Invoke</InvBadge>;
-};
-
-export const Default: Story = {
-  render: Component,
-};

invokeai/frontend/web/src/common/components/InvBadge/theme.ts

@@ -1,25 +0,0 @@
-import { defineStyle, defineStyleConfig } from '@chakra-ui/react';
-
-const baseStyle = defineStyle((props) => ({
-  fontSize: 9,
-  px: 2,
-  py: 1,
-  minW: 4,
-  lineHeight: 1,
-  borderRadius: 'sm',
-  bg: `${props.colorScheme}.200`,
-  color: 'base.900',
-  fontWeight: 'bold',
-  letterSpacing: 0.6,
-  wordBreak: 'break-all',
-  whiteSpace: 'nowrap',
-  textOverflow: 'ellipsis',
-  overflow: 'hidden',
-}));
-
-export const badgeTheme = defineStyleConfig({
-  baseStyle,
-  defaultProps: {
-    colorScheme: 'base',
-  },
-});

invokeai/frontend/web/src/common/components/InvBadge/types.ts

@@ -1,3 +0,0 @@
-import type { BadgeProps as ChakraBadgeProps } from '@chakra-ui/react';
-
-export type InvBadgeProps = ChakraBadgeProps;

invokeai/frontend/web/src/common/components/InvBadge/wrapper.ts

@@ -1 +0,0 @@
-export { Badge as InvBadge } from '@chakra-ui/react';

invokeai/frontend/web/src/common/components/InvButton/InvButton.stories.tsx

@@ -1,99 +0,0 @@
-import { Flex } from '@chakra-ui/layout';
-import type { Meta, StoryObj } from '@storybook/react';
-import { InvHeading } from 'common/components/InvHeading/wrapper';
-import { IoSparkles } from 'react-icons/io5';
-
-import { InvButton } from './InvButton';
-import type { InvButtonProps } from './types';
-
-const meta: Meta<typeof InvButton> = {
-  title: 'Primitives/InvButton',
-  tags: ['autodocs'],
-  component: InvButton,
-  parameters: {
-    controls: { expanded: true },
-  },
-  argTypes: {
-    isLoading: {
-      defaultValue: false,
-      control: { type: 'boolean' },
-    },
-    isDisabled: {
-      defaultValue: false,
-      control: { type: 'boolean' },
-    },
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof InvButton>;
-
-const colorSchemes = [
-  'base',
-  'invokeYellow',
-  'invokeRed',
-  'invokeGreen',
-  'invokeBlue',
-] as const;
-const variants = ['solid', 'outline', 'ghost', 'link'] as const;
-const sizes = ['xs', 'sm', 'md', 'lg'] as const;
-
-const Component = (props: InvButtonProps) => {
-  return (
-    <Flex gap={4} flexDir="column">
-      {sizes.map((size) => (
-        <>
-          <InvHeading>Size: {size}</InvHeading>
-
-          <Flex key={size} gap={4} flexDir="column">
-            {colorSchemes.map((colorScheme) => (
-              <Flex key={colorScheme} gap={4}>
-                {variants.map((variant) => (
-                  <>
-                    <InvButton
-                      size={size}
-                      key={`${variant}${colorScheme}`}
-                      variant={variant}
-                      colorScheme={colorScheme}
-                      {...props}
-                    >
-                      {variant}
-                    </InvButton>
-                    {['solid', 'outline'].includes(variant) && (
-                      <InvButton
-                        size={size}
-                        key={`${variant}${colorScheme}leftIcon`}
-                        variant={variant}
-                        colorScheme={colorScheme}
-                        leftIcon={<IoSparkles />}
-                        {...props}
-                      >
-                        {variant}
-                      </InvButton>
-                    )}
-                    {['solid', 'outline'].includes(variant) && (
-                      <InvButton
-                        size={size}
-                        key={`${variant}${colorScheme}rightIcon`}
-                        variant={variant}
-                        colorScheme={colorScheme}
-                        rightIcon={<IoSparkles />}
-                        {...props}
-                      >
-                        {variant}
-                      </InvButton>
-                    )}
-                  </>
-                ))}
-              </Flex>
-            ))}
-          </Flex>
-        </>
-      ))}
-    </Flex>
-  );
-};
-
-export const Default: Story = {
-  render: Component,
-};

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

@@ -1,37 +0,0 @@
-import { Button, forwardRef } from '@chakra-ui/react';
-import { InvTooltip } from 'common/components/InvTooltip/InvTooltip';
-import { memo } from 'react';
-
-import type { InvButtonProps } from './types';
-
-export const InvButton = memo(
-  forwardRef<InvButtonProps, typeof Button>(
-    ({ isChecked, tooltip, children, ...rest }: InvButtonProps, ref) => {
-      if (tooltip) {
-        return (
-          <InvTooltip label={tooltip}>
-            <Button
-              ref={ref}
-              colorScheme={isChecked ? 'invokeBlue' : 'base'}
-              {...rest}
-            >
-              {children}
-            </Button>
-          </InvTooltip>
-        );
-      }
-
-      return (
-        <Button
-          ref={ref}
-          colorScheme={isChecked ? 'invokeBlue' : 'base'}
-          {...rest}
-        >
-          {children}
-        </Button>
-      );
-    }
-  )
-);
-
-InvButton.displayName = 'InvButton';

invokeai/frontend/web/src/common/components/InvButton/theme.ts

@@ -1,228 +0,0 @@
-import type { StyleFunctionProps } from '@chakra-ui/react';
-import { defineStyle, defineStyleConfig } from '@chakra-ui/react';
-import { buttonVariantPromptOverlay } from 'features/parameters/components/Prompts/theme';
-
-type Variant = `solid` | `outline` | `ghost` | `link`;
-
-const getBorders = (
-  props: StyleFunctionProps,
-  variant: Variant
-): { borderWidth: string; borderStyle: string } => {
-  if (variant !== 'outline') {
-    return {
-      borderWidth: '0px',
-      borderStyle: 'none',
-    };
-  }
-  if (props.size === 'lg') {
-    return {
-      borderWidth: '1px',
-      borderStyle: 'solid',
-    };
-  }
-  if (props.size === 'md') {
-    return {
-      borderWidth: '1px',
-      borderStyle: 'solid',
-    };
-  }
-  return {
-    borderWidth: '1px',
-    borderStyle: 'solid',
-  };
-};
-
-const getColors = (
-  props: StyleFunctionProps,
-  variant: Variant
-): {
-  bg: string;
-  bgDisabled: string;
-  bgHover: string;
-  fg: string;
-  fgDisabled: string;
-  fgHover: string;
-  borderColor: string;
-  borderColorDisabled: string;
-  borderColorHover: string;
-} => {
-  const { colorScheme: c } = props;
-
-  const bgBase = 'base.400';
-  const bgColor = c === 'invokeYellow' ? `${c}.500` : `${c}.400`;
-  const bgBaseHover = 'base.300';
-  const bgColorHover = c === 'invokeYellow' ? `${c}.300` : `${c}.300`;
-
-  const notSolidFg = {
-    fg: c === 'base' ? 'base.300' : bgColor,
-    fgHover: c === 'base' ? 'base.50' : bgColorHover,
-    fgDisabled: 'base.600',
-  };
-  const noBg = { bg: 'none', bgHover: 'none', bgDisabled: 'none' };
-  const noBorder = {
-    borderColor: 'none',
-    borderColorDisabled: 'none',
-    borderColorHover: 'none',
-  };
-
-  if (variant === 'ghost') {
-    return {
-      ...notSolidFg,
-      ...noBg,
-      ...noBorder,
-      bgHover: 'baseAlpha.200',
-    };
-  }
-
-  if (variant === 'link') {
-    return {
-      ...notSolidFg,
-      ...noBg,
-      ...noBorder,
-    };
-  }
-  if (variant === 'outline') {
-    return {
-      ...notSolidFg,
-      ...noBg,
-      borderColor: c === 'invokeYellow' ? `${c}Alpha.500` : `${c}Alpha.400`,
-      borderColorDisabled: 'base.600',
-      borderColorHover:
-        c === 'invokeYellow' ? `${c}Alpha.700` : `${c}Alpha.600`,
-    };
-  }
-
-  // solid
-  return {
-    bg: c === 'base' ? bgBase : bgColor,
-    bgHover: c === 'base' ? bgBaseHover : bgColorHover,
-    bgDisabled: 'base.600',
-    fg: 'base.900',
-    fgHover: 'base.900',
-    fgDisabled: 'base.750',
-    ...noBorder,
-  };
-};
-
-const getStyles = (
-  props: StyleFunctionProps,
-  variant: 'ghost' | 'solid' | 'outline' | 'link'
-) => {
-  const { borderWidth, borderStyle } = getBorders(props, variant);
-  const {
-    bg,
-    bgDisabled,
-    bgHover,
-    fg,
-    fgDisabled,
-    fgHover,
-    borderColor,
-    borderColorDisabled,
-    borderColorHover,
-  } = getColors(props, variant);
-
-  const _disabled = {
-    bg: bgDisabled,
-    color: fgDisabled,
-    opacity: 1,
-    borderColor: borderColorDisabled,
-    svg: {
-      fill: fgDisabled,
-    },
-    _hover: {
-      bg: bgDisabled,
-      color: fgDisabled,
-      borderColor: borderColorDisabled,
-      svg: {
-        fill: fgDisabled,
-      },
-    },
-  };
-
-  const _base = {
-    bg: bg,
-    color: fg,
-    borderWidth: borderWidth,
-    borderStyle: borderStyle,
-    borderColor: borderColor,
-    svg: {
-      fill: fg,
-    },
-  };
-
-  return {
-    ..._base,
-    _hover: {
-      bg: bgHover,
-      color: fgHover,
-      borderColor: borderColorHover,
-      svg: {
-        fill: fgHover,
-      },
-      _disabled,
-    },
-    _active: { ..._base },
-    _disabled,
-  } as const;
-};
-
-export const buttonTheme = defineStyleConfig({
-  baseStyle: {
-    fontWeight: 'semibold',
-    svg: {
-      transitionProperty: 'all',
-      transitionDuration: 'faster',
-    },
-  },
-  sizes: {
-    sm: {
-      fontSize: 'sm',
-      px: 3,
-      py: 2,
-    },
-    md: {
-      fontSize: 'md',
-      px: 4,
-      py: 2,
-    },
-    lg: {
-      fontSize: 'lg',
-      px: 4,
-      py: 2,
-      h: 12,
-    },
-  },
-  variants: {
-    solid: defineStyle((props) => getStyles(props, 'solid')),
-    appTab: defineStyle((_props) => {
-      // Has no disabled, invalid, loading, etc state
-      return {
-        bg: 'none',
-        svg: {
-          fill: 'base.600',
-        },
-        _hover: {
-          bg: 'none',
-          svg: {
-            fill: 'base.400',
-          },
-        },
-        '&[data-selected="true"]': {
-          bg: 'none',
-          svg: {
-            fill: 'base.100',
-          },
-        },
-      };
-    }),
-    outline: defineStyle((props) => getStyles(props, 'outline')),
-    ghost: defineStyle((props) => getStyles(props, 'ghost')),
-    link: defineStyle((props) => getStyles(props, 'link')),
-    promptOverlay: buttonVariantPromptOverlay,
-  },
-  defaultProps: {
-    variant: 'solid',
-    colorScheme: 'base',
-    size: 'md',
-  },
-});

invokeai/frontend/web/src/common/components/InvButton/types.ts

@@ -1,7 +0,0 @@
-import type { ButtonProps } from '@chakra-ui/react';
-import type { ReactNode } from 'react';
-
-export type InvButtonProps = ButtonProps & {
-  isChecked?: boolean;
-  tooltip?: ReactNode;
-};

invokeai/frontend/web/src/common/components/InvButtonGroup/InvButtonGroup.stories.tsx

@@ -1,47 +0,0 @@
-import type { Meta, StoryObj } from '@storybook/react';
-import { InvButton } from 'common/components/InvButton/InvButton';
-import { InvIconButton } from 'common/components/InvIconButton/InvIconButton';
-import { FaImage } from 'react-icons/fa';
-
-import { InvButtonGroup } from './InvButtonGroup';
-import type { InvButtonGroupProps } from './types';
-
-const meta: Meta<typeof InvButtonGroup> = {
-  title: 'Primitives/InvButtonGroup',
-  tags: ['autodocs'],
-  component: InvButtonGroup,
-  args: {
-    colorScheme: 'base',
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof InvButtonGroup>;
-
-const Component = (props: InvButtonGroupProps) => {
-  return (
-    <InvButtonGroup {...props}>
-      <InvButton>Test</InvButton>
-      <InvButton>Test</InvButton>
-      <InvButton>Test</InvButton>
-    </InvButtonGroup>
-  );
-};
-
-const ComponentWithIconButtons = (props: InvButtonGroupProps) => {
-  return (
-    <InvButtonGroup {...props}>
-      <InvIconButton aria-label="test" icon={<FaImage />} />
-      <InvIconButton aria-label="test" icon={<FaImage />} />
-      <InvIconButton aria-label="test" icon={<FaImage />} />
-    </InvButtonGroup>
-  );
-};
-
-export const Default: Story = {
-  render: Component,
-};
-
-export const WithIconButtons: Story = {
-  render: ComponentWithIconButtons,
-};

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

@@ -1,14 +0,0 @@
-import { ButtonGroup, forwardRef } from '@chakra-ui/react';
-import { memo } from 'react';
-
-import type { InvButtonGroupProps } from './types';
-
-export const InvButtonGroup = memo(
-  forwardRef<InvButtonGroupProps, typeof ButtonGroup>(
-    ({ isAttached = true, ...rest }: InvButtonGroupProps, ref) => {
-      return <ButtonGroup ref={ref} isAttached={isAttached} {...rest} />;
-    }
-  )
-);
-
-InvButtonGroup.displayName = 'InvButtonGroup';