Update diffuses to 0.25

.github/CODEOWNERS

@@ -1,5 +1,5 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername
 
 # documentation
 /docs/ @lstein @blessedcoolant @hipsterusername @Millu
@@ -10,7 +10,7 @@
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant @hipsterusername
-/docker/  @lstein @blessedcoolant @hipsterusername @ebr
+/docker/  @lstein @blessedcoolant @hipsterusername
 /scripts/ @ebr @lstein @hipsterusername
 /installer/ @lstein @ebr @hipsterusername
 /invokeai/assets @lstein @ebr @hipsterusername
@@ -26,7 +26,9 @@
 
 # front ends
 /invokeai/frontend/CLI @lstein @hipsterusername
-/invokeai/frontend/install @lstein @ebr @hipsterusername
+/invokeai/frontend/install @lstein @ebr @hipsterusername 
 /invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername
+
+

.github/pr_labels.yml

@@ -1,59 +0,0 @@
-Root:
-- changed-files:
-  - any-glob-to-any-file: '*'
-
-PythonDeps:
-- changed-files:
-  - any-glob-to-any-file: 'pyproject.toml'
-
-Python:
-- changed-files:
-  - all-globs-to-any-file: 
-    - 'invokeai/**'
-    - '!invokeai/frontend/web/**'
-
-PythonTests:
-- changed-files:
-  - any-glob-to-any-file: 'tests/**'
-
-CICD:
-- changed-files:
-  - any-glob-to-any-file: .github/**
-
-Docker:
-- changed-files:
-  - any-glob-to-any-file: docker/**
-
-Installer:
-- changed-files:
-  - any-glob-to-any-file: installer/**
-
-Documentation:
-- changed-files:
-  - any-glob-to-any-file: docs/**
-
-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/build-container.yml

@@ -40,14 +40,10 @@ jobs:
       - name: Free up more disk space on the runner
         # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
         run: |
-          echo "----- Free space before cleanup"
-          df -h
           sudo rm -rf /usr/share/dotnet
           sudo rm -rf "$AGENT_TOOLSDIRECTORY"
           sudo swapoff /mnt/swapfile
           sudo rm -rf /mnt/swapfile
-          echo "----- Free space after cleanup"
-          df -h
 
       - name: Checkout
         uses: actions/checkout@v3
@@ -95,7 +91,6 @@ jobs:
       #     password: ${{ secrets.DOCKERHUB_TOKEN }}
 
       - name: Build container
-        timeout-minutes: 40
         id: docker_build
         uses: docker/build-push-action@v4
         with:

.github/workflows/label-pr.yml

@@ -1,16 +0,0 @@
-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@v41
+        uses: tj-actions/changed-files@v37
         with:
           files_yaml: |
             python:

README.md

@@ -1,10 +1,10 @@
 <div align="center">
 
-![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)
+![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/1a917d94-e099-4fa1-a70f-7dd8d0691018)
 
-# Invoke - Professional Creative AI Tools for Visual Media 
-##  To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
-  
+# Invoke AI - Generative AI for Professional Creatives
+## Professional Creative Tools for Stable Diffusion, Custom-Trained Models, and more. 
+  To learn more about Invoke AI, get started instantly, or implement our Business solutions, visit [invoke.ai](https://invoke.ai)
 
 
 [![discord badge]][discord link]
@@ -56,9 +56,7 @@ the foundation for multiple commercial products.
 
 <div align="center">
 
-
-![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
-
+![canvas preview](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/canvas_preview.png)
 
 </div>
 

docker/.env.sample

@@ -2,13 +2,10 @@
 ## Any environment variables supported by InvokeAI can be specified here,
 ## in addition to the examples below.
 
-# HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where InvokeAI will store data.
+# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
 # Outputs will also be stored here by default.
-# If relative, it will be relative to the docker directory in which the docker-compose.yml file is located
-#HOST_INVOKEAI_ROOT=../../invokeai-data
-
-# INVOKEAI_ROOT is the path to the root of the InvokeAI repository within the container.
-# INVOKEAI_ROOT=~/invokeai
+# This **must** be an absolute path.
+INVOKEAI_ROOT=
 
 # Get this value from your HuggingFace account settings page.
 # HUGGING_FACE_HUB_TOKEN=

docker/Dockerfile

@@ -59,7 +59,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 
 # #### Build the Web UI ------------------------------------
 
-FROM node:20-slim AS web-builder
+FROM node:18-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
 ENV PATH="$PNPM_HOME:$PATH"
 RUN corepack enable
@@ -68,7 +68,7 @@ WORKDIR /build
 COPY invokeai/frontend/web/ ./
 RUN --mount=type=cache,target=/pnpm/store \
     pnpm install --frozen-lockfile
-RUN npx vite build
+RUN pnpm run build
 
 #### Runtime stage ---------------------------------------
 

docker/docker-compose.yml

@@ -21,9 +21,7 @@ x-invokeai: &invokeai
     ports:
       - "${INVOKEAI_PORT:-9090}:9090"
     volumes:
-      - type: bind
-        source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
-        target: ${INVOKEAI_ROOT:-/invokeai}
+      - ${INVOKEAI_ROOT:-~/invokeai}:${INVOKEAI_ROOT:-/invokeai}
       - ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
       # - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
       # - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}

docs/contributing/MODEL_MANAGER.md

@@ -15,13 +15,8 @@ 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.
-  
-* _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_
+
+* _DownloadQueueServiceBase_ (**CURRENTLY UNDER DEVELOPMENT - NOT IMPLEMENTED**)
   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
@@ -35,13 +30,13 @@ model. These are the:
   
 ## Location of the Code
 
-The four main services can be found in
+All four of these 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`.
@@ -407,18 +402,15 @@ functionality:
   the download, installation and registration process.
   
 - Downloading a model from an arbitrary URL and installing it in
-  `models_dir`.
+  `models_dir` (_implementation pending_).
   
 - Special handling for Civitai model URLs which allow the user to
-  paste in a model page's URL or download link
+  paste in a model page's URL or download link (_implementation pending_).
+
   
 - Special handling for HuggingFace repo_ids to recursively download
   the contents of the repository, paying attention to alternative
-  variants such as fp16.
-  
-- Saving tags and other metadata about the model into the invokeai database
-  when fetching from a repo that provides that type of information,
-  (currently only Civitai and HuggingFace).
+  variants such as fp16. (_implementation pending_)
   
 ### Initializing the installer
 
@@ -434,24 +426,16 @@ 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()
 
-installer = ModelInstallService(app_config=config, 
-                                record_store=record_store,
-						        download_queue=queue
-							    )
-installer.start()
+store = ModelRecordServiceSQL(db)
+installer = ModelInstallService(config, store)
 ```
 
 The full form of `ModelInstallService()` takes the following
@@ -459,12 +443,9 @@ required parameters:
 
 | **Argument**     | **Type**                     | **Description**              |
 |------------------|------------------------------|------------------------------|
-| `app_config`         | InvokeAIAppConfig       | InvokeAI app configuration object |
+| `config`         | InvokeAIAppConfig       | InvokeAI app configuration object |
 | `record_store`   | ModelRecordServiceBase  | Config record storage database |
-| `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) |
-
+| `event_bus`      | EventServiceBase        | Optional event bus to send download/install progress events to |
 
 Once initialized, the installer will provide the following methods:
 
@@ -493,14 +474,14 @@ 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(timeout=120)
+source2job = installer.wait_for_installs()
 for source in sources:
     job = source2job[source]
-	if job.complete:
+	if job.status == "completed":
 		model_config = job.config_out
 		model_key = model_config.key
 		print(f"{source} installed as {model_key}")
-	elif job.errored:
+	elif job.status == "error":
 	    print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
 	
 ```
@@ -534,117 +515,43 @@ The full list of arguments to `import_model()` is as follows:
 
 | **Argument**     | **Type**                     | **Default** | **Description**                           |
 |------------------|------------------------------|-------------|-------------------------------------------|
-| `source`         | ModelSource                 | None        | The source of the model, Path, URL or repo_id |
+| `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)   |
 | `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 next few sections describe the various types of ModelSource that
-can be passed to `import_model()`. 
+
+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.
 
 `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.
 
-
-#### 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.
-
+`access_token` is passed to the download queue and used to access
+repositories that require it.
 
 #### Monitoring the install job process
 
@@ -656,8 +563,7 @@ The `ModelInstallJob` class has the following structure:
 
 | **Attribute** | **Type**        |  **Description** |
 |----------------|-----------------|------------------|
-| `id`           | `int`           | Integer ID for this job |
-| `status`       | `InstallStatus`  | An enum of [`waiting`, `downloading`, `running`, `completed`, `error` and `cancelled`]|
+| `status`       | `InstallStatus`  | An enum of ["waiting", "running", "completed" and "error" |
 | `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 | 
@@ -672,70 +578,30 @@ 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_downloading`
+- `model_install_started`
 
-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:
+The payload will contain the keys `timestamp` and `source`. The latter
+indicates the requested model source for installation.
 
-| **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 |
+- `model_install_progress`
 
+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.
 
-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.
+- `model_install_completed`
 
-Note that downloading events will not be issued for local models, and
-that downloading events occur *before* the running 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.
 
-##### `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.
+- `model_install_error`
 
+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
 
@@ -755,9 +621,17 @@ 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=HFModelSource(repo_id='stabilityai/stable-diffusion-2-1',variant='fp32'),
+               source='stabilityai/stable-diffusion-2-1',
+			   variant='fp16',
 			   config=dict(
 			         prediction_type=SchedulerPredictionType('v_prediction')
 					 name='stable diffusion 2 base model',
@@ -769,38 +643,29 @@ install_job = installer.import_model(
 
 This section describes additional methods provided by the installer class.
 
-#### jobs = installer.wait_for_installs([timeout])
+#### jobs = installer.wait_for_installs()
 
 Block until all pending installs are completed or errored and then
-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.
+returns a list of completed jobs.
 
-#### jobs = installer.list_jobs()
+#### jobs = installer.list_jobs([source])
 
-Return a list of all active and complete `ModelInstallJobs`.
+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.
 
-#### jobs = installer.get_job_by_source(source)
+#### jobs = installer.get_job(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 jobs that are in a terminal state (i.e. complete, errored or
-cancelled) from the job list returned by `list_jobs()` and
-`get_job()`.
+Remove non-pending jobs (completed or errored) 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.
@@ -861,6 +726,120 @@ 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
@@ -900,6 +879,7 @@ 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
@@ -1204,362 +1184,3 @@ 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/contributing/contribution_guides/contributingToFrontend.md

@@ -0,0 +1,76 @@
+# Contributing to the Frontend
+
+# InvokeAI Web UI
+
+- [InvokeAI Web UI](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#invokeai-web-ui)
+    - [Stack](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#stack)
+    - [Contributing](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#contributing)
+        - [Dev Environment](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#dev-environment)
+        - [Production builds](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#production-builds)
+
+The UI is a fairly straightforward Typescript React app, with the Unified Canvas being more complex.
+
+Code is located in `invokeai/frontend/web/` for review.
+
+## Stack
+
+State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
+
+- `createAsyncThunk` for HTTP requests
+- `createEntityAdapter` for fetching images and models
+- `createListenerMiddleware` for workflows
+
+The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
+
+Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
+
+[Chakra-UI](https://github.com/chakra-ui/chakra-ui) & [Mantine](https://github.com/mantinedev/mantine) for components and styling.
+
+[Konva](https://github.com/konvajs/react-konva) for the canvas, but we are pushing the limits of what is feasible with it (and HTML canvas in general). We plan to rebuild it with [PixiJS](https://github.com/pixijs/pixijs) to take advantage of WebGL's improved raster handling.
+
+[Vite](https://vitejs.dev/) for bundling.
+
+Localisation is via [i18next](https://github.com/i18next/react-i18next), but translation happens on our [Weblate](https://hosted.weblate.org/engage/invokeai/) project. Only the English source strings should be changed on this repo.
+
+## Contributing
+
+Thanks for your interest in contributing to the InvokeAI Web UI!
+
+We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](https://discord.gg/ZmtBAhwWhy) if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
+
+### Dev Environment
+
+**Setup** 
+
+1. Install [node](https://nodejs.org/en/download/). You can confirm node is installed with:
+```bash
+node --version
+```
+
+2. Install [pnpm](https://pnpm.io/) and confirm it is installed by running this:
+```bash
+npm install --global pnpm
+pnpm --version
+```
+
+From `invokeai/frontend/web/` run `pnpm install` to get everything set up.
+
+Start everything in dev mode:
+1. Ensure your virtual environment is running
+2. Start the dev server: `pnpm dev`
+3. Start the InvokeAI Nodes backend: `python scripts/invokeai-web.py # run from the repo root`
+4. Point your browser to the dev server address e.g. [http://localhost:5173/](http://localhost:5173/)
+
+### VSCode Remote Dev
+
+We've noticed an intermittent issue with the VSCode Remote Dev port forwarding. If you use this feature of VSCode, you may intermittently click the Invoke button and then get nothing until the request times out. Suggest disabling the IDE's port forwarding feature and doing it manually via SSH:
+
+`ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host`
+
+### Production builds
+
+For a number of technical and logistical reasons, we need to commit UI build artefacts to the repo.
+
+If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
+
+To build for production, run `pnpm build`.

docs/contributing/contribution_guides/development.md

@@ -12,7 +12,7 @@ To get started, take a look at our [new contributors checklist](newContributorCh
 Once you're setup, for more information, you can review the documentation specific to your area of interest:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web)
+* #### [Frontend Documentation](./contributingToFrontend.md)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 

docs/deprecated/2to3.md

@@ -1,53 +0,0 @@
-## :octicons-log-16: Important Changes Since Version 2.3
-
-### Nodes
-
-Behind the scenes, InvokeAI has been completely rewritten to support
-"nodes," small unitary operations that can be combined into graphs to
-form arbitrary workflows. For example, there is a prompt node that
-processes the prompt string and feeds it to a text2latent node that
-generates a latent image. The latents are then fed to a latent2image
-node that translates the latent image into a PNG.
-
-The WebGUI has a node editor that allows you to graphically design and
-execute custom node graphs. The ability to save and load graphs is
-still a work in progress, but coming soon.
-
-### Command-Line Interface Retired
-
-All "invokeai" command-line interfaces have been retired as of version
-3.4.
-
-To launch the Web GUI from the command-line, use the command
-`invokeai-web` rather than the traditional `invokeai --web`.
-
-### ControlNet
-
-This version of InvokeAI features ControlNet, a system that allows you
-to achieve exact poses for human and animal figures by providing a
-model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
-
-### New Schedulers
-
-The list of schedulers has been completely revamped and brought up to date:
-
-| **Short Name** | **Scheduler**                   | **Notes**                   |
-|----------------|---------------------------------|-----------------------------|
-| **ddim**       | DDIMScheduler                   |                             |
-| **ddpm**       | DDPMScheduler                   |                             |
-| **deis**       | DEISMultistepScheduler          |                             |
-| **lms**        | LMSDiscreteScheduler            |                             |
-| **pndm**       | PNDMScheduler                   |                             |
-| **heun**       | HeunDiscreteScheduler           | original noise schedule     |
-| **heun_k**     | HeunDiscreteScheduler           | using karras noise schedule |
-| **euler**      | EulerDiscreteScheduler          | original noise schedule     |
-| **euler_k**    | EulerDiscreteScheduler          | using karras noise schedule |
-| **kdpm_2**     | KDPM2DiscreteScheduler          |                             |
-| **kdpm_2_a**   | KDPM2AncestralDiscreteScheduler |                             |
-| **dpmpp_2s**   | DPMSolverSinglestepScheduler    |                             |
-| **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
-| **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
-| **unipc**      | UniPCMultistepScheduler         | CPU only                    |
-| **lcm**        | LCMScheduler                    |                             |
-
-Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.

docs/features/UNIFIED_CANVAS.md

@@ -229,28 +229,29 @@ clarity on the intent and common use cases we expect for utilizing them.
   currently being rendered by your browser into a merged copy of the image. This
   lowers the resource requirements and should improve performance.
 
-### Compositing / Seam Correction
+### Seam Correction
 
 When doing Inpainting or Outpainting, Invoke needs to merge the pixels generated
-by Stable Diffusion into your existing image. This is achieved through compositing - the area around the the boundary between your image and the new generation is
+by Stable Diffusion into your existing image. To do this, the area around the
+`seam` at the boundary between your image and the new generation is
 automatically blended to produce a seamless output. In a fully automatic
-process, a mask is generated to cover the boundary, and then the area of the boundary is
+process, a mask is generated to cover the seam, and then the area of the seam is
 Inpainted.
 
 Although the default options should work well most of the time, sometimes it can
-help to alter the parameters that control the Compositing. A larger blur and
-a blur setting  have been noted as producing
-consistently strong results . Strength of 0.7 is best for reducing hard seams.
-
-- **Mode** - What part of the image will have the the Compositing applied to it.
-  - **Mask edge** will apply Compositing to the edge of the masked area
-  - **Mask** will apply Compositing to the entire masked area
-  - **Unmasked** will apply Compositing to the entire image
-- **Steps** - Number of generation steps that will occur during the Coherence Pass, similar to Denoising Steps. Higher step counts will generally have better results.
-- **Strength** - How much noise is added for the Coherence Pass, similar to Denoising Strength. A strength of 0 will result in an unchanged image, while a strength of 1 will result in an image with a completely new area as defined by the Mode setting.
-- **Blur** - Adjusts the pixel radius of the the mask. A larger blur radius will cause the mask to extend past the visibly masked area, while too small of a blur radius will result in a mask that is smaller than the visibly masked area.
-- **Blur Method** - The method of blur applied to the masked area. 
+help to alter the parameters that control the seam Inpainting. A wider seam and
+a blur setting of about 1/3 of the seam have been noted as producing
+consistently strong results (e.g. 96 wide and 16 blur - adds up to 32 blur with
+both sides). Seam strength of 0.7 is best for reducing hard seams.
 
+- **Seam Size** - The size of the seam masked area. Set higher to make a larger
+  mask around the seam.
+- **Seam Blur** - The size of the blur that is applied on _each_ side of the
+  masked area.
+- **Seam Strength** - The Image To Image Strength parameter used for the
+  Inpainting generation that is applied to the seam area.
+- **Seam Steps** - The number of generation steps that should be used to Inpaint
+  the seam.
 
 ### Infill & Scaling
 

docs/index.md

@@ -18,7 +18,7 @@ title: Home
       width: 100%;
       max-width: 100%;
       height: 50px;
-      background-color: #35A4DB;
+      background-color: #448AFF;
       color: #fff;
       font-size: 16px;
       border: none;
@@ -43,7 +43,7 @@ title: Home
 <div align="center" markdown>
 
 
-[![project logo](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)](https://github.com/invoke-ai/InvokeAI)
+[![project logo](assets/invoke_ai_banner.png)](https://github.com/invoke-ai/InvokeAI)
 
 [![discord badge]][discord link]
 
@@ -145,6 +145,60 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 - [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
 - [Database Maintenance and other Command Line Utilities](features/UTILITIES.md)
 
+## :octicons-log-16: Important Changes Since Version 2.3
+
+### Nodes
+
+Behind the scenes, InvokeAI has been completely rewritten to support
+"nodes," small unitary operations that can be combined into graphs to
+form arbitrary workflows. For example, there is a prompt node that
+processes the prompt string and feeds it to a text2latent node that
+generates a latent image. The latents are then fed to a latent2image
+node that translates the latent image into a PNG.
+
+The WebGUI has a node editor that allows you to graphically design and
+execute custom node graphs. The ability to save and load graphs is
+still a work in progress, but coming soon.
+
+### Command-Line Interface Retired
+
+All "invokeai" command-line interfaces have been retired as of version
+3.4.
+
+To launch the Web GUI from the command-line, use the command
+`invokeai-web` rather than the traditional `invokeai --web`.
+
+### ControlNet
+
+This version of InvokeAI features ControlNet, a system that allows you
+to achieve exact poses for human and animal figures by providing a
+model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
+
+### New Schedulers
+
+The list of schedulers has been completely revamped and brought up to date:
+
+| **Short Name** | **Scheduler**                   | **Notes**                   |
+|----------------|---------------------------------|-----------------------------|
+| **ddim**       | DDIMScheduler                   |                             |
+| **ddpm**       | DDPMScheduler                   |                             |
+| **deis**       | DEISMultistepScheduler          |                             |
+| **lms**        | LMSDiscreteScheduler            |                             |
+| **pndm**       | PNDMScheduler                   |                             |
+| **heun**       | HeunDiscreteScheduler           | original noise schedule     |
+| **heun_k**     | HeunDiscreteScheduler           | using karras noise schedule |
+| **euler**      | EulerDiscreteScheduler          | original noise schedule     |
+| **euler_k**    | EulerDiscreteScheduler          | using karras noise schedule |
+| **kdpm_2**     | KDPM2DiscreteScheduler          |                             |
+| **kdpm_2_a**   | KDPM2AncestralDiscreteScheduler |                             |
+| **dpmpp_2s**   | DPMSolverSinglestepScheduler    |                             |
+| **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
+| **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
+| **unipc**      | UniPCMultistepScheduler         | CPU only                    |
+| **lcm**        | LCMScheduler                    |                             |
+
+Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
+
 ## :material-target: Troubleshooting
 
 Please check out our **[:material-frequently-asked-questions:

docs/nodes/NODES.md

@@ -6,17 +6,10 @@ If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](
 
 ## Features
 
-### Workflow Library
-The Workflow Library enables you to save workflows to the Invoke database, allowing you to easily creating, modify and share workflows as needed. 
-
-A curated set of workflows are provided by default - these are designed to help explain important nodes' usage in the Workflow Editor.
-
-![workflow_library](../assets/nodes/workflow_library.png)
-
 ### Linear View
 The Workflow Editor allows you to create a UI for your workflow, to make it easier to iterate on your generations. 
 
-To add an input to the Linear UI, right click on the **input label** and select "Add to Linear View".
+To add an input to the Linear UI, right click on the input label and select "Add to Linear View".
 
 The Linear UI View will also be part of the saved workflow, allowing you share workflows and enable other to use them, regardless of complexity. 
 
@@ -37,7 +30,7 @@ Any node or input field can be renamed in the workflow editor. If the input fiel
 Nodes have a "Use Cache" option in their footer. This allows for performance improvements by using the previously cached values during the workflow processing. 
 
 
-## Important Nodes & Concepts 
+## Important Concepts 
 
 There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
 
@@ -63,7 +56,7 @@ The ImageToLatents node takes in a pixel image and a VAE and outputs a latents.
 
 It is common to want to use both the same seed (for continuity) and random seeds (for variety). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
 
-![groupsrandseed](../assets/nodes/groupsnoise.png)
+![groupsrandseed](../assets/nodes/groupsrandseed.png)
 
 ### ControlNet
 

docs/nodes/communityNodes.md

@@ -25,6 +25,7 @@ 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)
@@ -35,8 +36,7 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Mask Operations](#mask-operations)
     + [Match Histogram](#match-histogram)
     + [Metadata-Linked](#metadata-linked-nodes)
-    + [Negative Image](#negative-image)
-    + [Nightmare Promptgen](#nightmare-promptgen)    
+    + [Negative Image](#negative-image)    
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
     + [Remote Image](#remote-image)
@@ -195,6 +195,13 @@ 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
 
@@ -339,13 +346,6 @@ Node Link: https://github.com/VeyDlin/negative-image-node
 View:
 </br><img src="https://raw.githubusercontent.com/VeyDlin/negative-image-node/master/.readme/node.png" width="500" />
 
---------------------------------
-### Nightmare Promptgen
-
-**Description:** Nightmare Prompt Generator - Uses a local text generation model to create unique imaginative (but usually nightmarish) prompts for InvokeAI. By default, it allows you to choose from some gpt-neo models I finetuned on over 2500 of my own InvokeAI prompts in Compel format, but you're able to add your own, as well. Offers support for replacing any troublesome words with a random choice from list you can also define.
-
-**Node Link:** [https://github.com/gogurtenjoyer/nightmare-promptgen](https://github.com/gogurtenjoyer/nightmare-promptgen)
-
 --------------------------------
 ### Oobabooga
 

docs/nodes/defaultNodes.md

@@ -36,7 +36,6 @@ 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                                                                                                                             |

docs/nodes/exampleWorkflows.md

@@ -1,6 +1,6 @@
 # Example Workflows
 
-We've curated some example workflows for you to get started with Workflows in InvokeAI! These can also be found in the Workflow Library, located in the Workflow Editor of Invoke.
+We've curated some example workflows for you to get started with Workflows in InvokeAI
 
 To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
 

docs/other/CONTRIBUTORS.md

@@ -215,7 +215,6 @@ We thank them for all of their time and hard work.
 - Robert Bolender
 - Robin Rombach
 - Rohan Barar
-- rohinish404
 - rpagliuca
 - rromb
 - Rupesh Sreeraman

docs/stylesheets/extra.css

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

installer/lib/installer.py

@@ -241,12 +241,12 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
-                "numpy==1.26.3",  # choose versions that won't be uninstalled during phase 2
+                "numpy~=1.24.0",  # choose versions that won't be uninstalled during phase 2
                 "urllib3~=1.26.0",
                 "requests~=2.28.0",
                 "torch==2.1.2",
                 "torchmetrics==0.11.4",
-                "torchvision==0.16.2",
+                "torchvision>=0.16.2",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

invokeai/app/api/dependencies.py

@@ -3,7 +3,6 @@
 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__
 
@@ -62,7 +61,7 @@ class ApiDependencies:
     invoker: Invoker
 
     @staticmethod
-    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger) -> None:
+    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
         logger.info(f"InvokeAI version {__version__}")
         logger.info(f"Root directory = {str(config.root_path)}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
@@ -88,13 +87,8 @@ 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,
-            download_queue=download_queue_service,
-            metadata_store=metadata_store,
-            event_bus=events,
+            app_config=config, record_store=model_record_service, event_bus=events
         )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
@@ -137,6 +131,6 @@ class ApiDependencies:
         db.clean()
 
     @staticmethod
-    def shutdown() -> None:
+    def shutdown():
         if ApiDependencies.invoker:
             ApiDependencies.invoker.stop()

invokeai/app/api/no_cache_staticfiles.py

@@ -1,28 +0,0 @@
-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/model_records.py

@@ -4,7 +4,7 @@
 
 from hashlib import sha1
 from random import randbytes
-from typing import Any, Dict, List, Optional, Set
+from typing import Any, Dict, List, Optional
 
 from fastapi import Body, Path, Query, Response
 from fastapi.routing import APIRouter
@@ -16,18 +16,13 @@ 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
 
@@ -37,20 +32,11 @@ 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",
@@ -59,7 +45,7 @@ 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[ModelFormat] = Query(
+    model_format: Optional[str] = Query(
         default=None, description="Exact match on the format of the model (e.g. 'diffusers')"
     ),
 ) -> ModelsList:
@@ -100,59 +86,6 @@ 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",
@@ -226,7 +159,9 @@ 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>":
@@ -308,7 +243,7 @@ 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_running"
+      "model_install_started"
       "model_install_completed"
       "model_install_error"
 
@@ -344,46 +279,16 @@ async def import_model(
     operation_id="list_model_install_jobs",
 )
 async def list_model_install_jobs() -> List[ModelInstallJob]:
-    """Return list of model install jobs."""
+    """
+    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.
+    """
     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",
@@ -393,7 +298,9 @@ async def cancel_model_install_job(id: int = Path(description="Model install job
     },
 )
 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)
 
@@ -408,9 +315,7 @@ 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/routers/utilities.py

@@ -23,11 +23,10 @@ class DynamicPromptsResponse(BaseModel):
 )
 async def parse_dynamicprompts(
     prompt: str = Body(description="The prompt to parse with dynamicprompts"),
-    max_prompts: int = Body(ge=1, le=10000, default=1000, description="The max number of prompts to generate"),
+    max_prompts: int = Body(default=1000, description="The max number of prompts to generate"),
     combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
 ) -> DynamicPromptsResponse:
     """Creates a batch process"""
-    max_prompts = min(max_prompts, 10000)
     generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator]
     try:
         error: Optional[str] = None

invokeai/app/api_app.py

@@ -3,7 +3,6 @@
 # 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
@@ -28,7 +27,8 @@ 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 HTMLResponse
+    from fastapi.responses import FileResponse, HTMLResponse
+    from fastapi.staticfiles import StaticFiles
     from fastapi_events.handlers.local import local_handler
     from fastapi_events.middleware import EventHandlerASGIMiddleware
     from pydantic.json_schema import models_json_schema
@@ -76,7 +76,7 @@ mimetypes.add_type("text/css", ".css")
 
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
-app = FastAPI(title="Invoke - Community Edition", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
+app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
 
 # Add event handler
 event_handler_id: int = id(app)
@@ -205,8 +205,8 @@ app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid a
 def overridden_swagger() -> HTMLResponse:
     return get_swagger_ui_html(
         openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=f"{app.title} - Swagger UI",
-        swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
+        title=app.title,
+        swagger_favicon_url="/static/docs/favicon.ico",
     )
 
 
@@ -214,20 +214,26 @@ def overridden_swagger() -> HTMLResponse:
 def overridden_redoc() -> HTMLResponse:
     return get_redoc_html(
         openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=f"{app.title} - Redoc",
-        redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
+        title=app.title,
+        redoc_favicon_url="/static/docs/favicon.ico",
     )
 
 
 web_root_path = Path(list(web_dir.__path__)[0])
 
-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
+# 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
 
 
 def invoke_api() -> None:

invokeai/app/invocations/controlnet_image_processors.py

@@ -24,13 +24,11 @@ from controlnet_aux import (
 )
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
@@ -77,16 +75,17 @@ class ControlField(BaseModel):
     resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
     @field_validator("control_weight")
-    @classmethod
     def validate_control_weight(cls, v):
-        validate_weights(v)
+        """Validate that all control weights in the valid range"""
+        if isinstance(v, list):
+            for i in v:
+                if i < -1 or i > 2:
+                    raise ValueError("Control weights must be within -1 to 2 range")
+        else:
+            if v < -1 or v > 2:
+                raise ValueError("Control weights must be within -1 to 2 range")
         return v
 
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
 
 @invocation_output("control_output")
 class ControlOutput(BaseInvocationOutput):
@@ -96,17 +95,17 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
     control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
     control_weight: Union[float, List[float]] = InputField(
-        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+        default=1.0, description="The weight given to the ControlNet"
     )
     begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+        default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
     )
     end_step_percent: float = InputField(
         default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
@@ -114,17 +113,6 @@ class ControlNetInvocation(BaseInvocation):
     control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
     resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
 
-    @field_validator("control_weight")
-    @classmethod
-    def validate_control_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> "ControlNetInvocation":
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
     def invoke(self, context: InvocationContext) -> ControlOutput:
         return ControlOutput(
             control=ControlField(
@@ -603,33 +591,3 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
         color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
         color_map = Image.fromarray(color_map)
         return color_map
-
-
-DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
-
-
-@invocation(
-    "depth_anything_image_processor",
-    title="Depth Anything Processor",
-    tags=["controlnet", "depth", "depth anything"],
-    category="controlnet",
-    version="1.0.0",
-)
-class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
-    """Generates a depth map based on the Depth Anything algorithm"""
-
-    model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
-        default="small", description="The size of the depth model to use"
-    )
-    resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
-    offload: bool = InputField(default=False)
-
-    def run_processor(self, image):
-        depth_anything_detector = DepthAnythingDetector()
-        depth_anything_detector.load_model(model_size=self.model_size)
-
-        if image.mode == "RGBA":
-            image = image.convert("RGB")
-
-        processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
-        return processed_image

invokeai/app/invocations/ip_adapter.py

@@ -2,7 +2,7 @@ import os
 from builtins import float
 from typing import List, Union
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -15,7 +15,6 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType, ModelType
 from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
@@ -40,6 +39,7 @@ class IPAdapterField(BaseModel):
     ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
     image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
+    # weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
     )
@@ -47,17 +47,6 @@ class IPAdapterField(BaseModel):
         default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
     )
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
 
 @invocation_output("ip_adapter_output")
 class IPAdapterOutput(BaseInvocationOutput):
@@ -65,7 +54,7 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.1")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -75,27 +64,18 @@ class IPAdapterInvocation(BaseInvocation):
         description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
     )
 
+    # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
     weight: Union[float, List[float]] = InputField(
-        default=1, description="The weight given to the IP-Adapter", title="Weight"
+        default=1, ge=-1, description="The weight given to the IP-Adapter", title="Weight"
     )
+
     begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
+        default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
     )
     end_step_percent: float = InputField(
         default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
     )
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
     def invoke(self, context: InvocationContext) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
         ip_adapter_info = context.services.model_manager.model_info(

invokeai/app/invocations/latent.py

@@ -1,6 +1,5 @@
 # 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
@@ -221,7 +220,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.1",
+    version="1.5.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -280,7 +279,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         ui_order=7,
     )
     cfg_rescale_multiplier: float = InputField(
-        title="CFG Rescale Multiplier", default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
     )
     latents: Optional[LatentsField] = InputField(
         default=None,
@@ -1229,57 +1228,3 @@ 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/invocations/t2i_adapter.py

@@ -1,6 +1,6 @@
 from typing import Union
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -14,7 +14,6 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType
 
@@ -38,17 +37,6 @@ class T2IAdapterField(BaseModel):
     )
     resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
 
 @invocation_output("t2i_adapter_output")
 class T2IAdapterOutput(BaseInvocationOutput):
@@ -56,7 +44,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.1"
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.0"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""
@@ -73,7 +61,7 @@ class T2IAdapterInvocation(BaseInvocation):
         default=1, ge=0, description="The weight given to the T2I-Adapter", title="Weight"
     )
     begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
+        default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"
     )
     end_step_percent: float = InputField(
         default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
@@ -83,17 +71,6 @@ class T2IAdapterInvocation(BaseInvocation):
         description="The resize mode applied to the T2I-Adapter input image so that it matches the target output size.",
     )
 
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
     def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
         return T2IAdapterOutput(
             t2i_adapter=T2IAdapterField(

invokeai/app/invocations/util.py

@@ -1,14 +0,0 @@
-from typing import Union
-
-
-def validate_weights(weights: Union[float, list[float]]) -> None:
-    """Validate that all control weights in the valid range"""
-    to_validate = weights if isinstance(weights, list) else [weights]
-    if any(i < -1 or i > 2 for i in to_validate):
-        raise ValueError("Control weights must be within -1 to 2 range")
-
-
-def validate_begin_end_step(begin_step_percent: float, end_step_percent: float) -> None:
-    """Validate that begin_step_percent is less than end_step_percent"""
-    if begin_step_percent >= end_step_percent:
-        raise ValueError("Begin step percent must be less than or equal to end step percent")

invokeai/app/services/config/__init__.py

@@ -1,7 +1,5 @@
 """Init file for InvokeAI configure package."""
 
-from invokeai.app.services.config.config_common import PagingArgumentParser
-
 from .config_default import InvokeAIAppConfig, get_invokeai_config
 
-__all__ = ["InvokeAIAppConfig", "get_invokeai_config", "PagingArgumentParser"]
+__all__ = ["InvokeAIAppConfig", "get_invokeai_config"]

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[str, Any]]] = None
+    singleton_init: ClassVar[Optional[Dict]] = 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", "bfloat16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
+    precision           : Literal["auto", "float16", "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: Optional[bool] = False,
-    ) -> None:
+        clobber=False,
+    ):
         """
         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: Any) -> InvokeAIAppConfig:
+    def get_config(cls, **kwargs: Dict[str, 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: Any) -> InvokeAIAppConfig:
+def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
     """Legacy function which returns InvokeAIAppConfig.get_config()."""
     return InvokeAIAppConfig.get_config(**kwargs)
 

invokeai/app/services/download/download_base.py

@@ -34,7 +34,6 @@ class ServiceInactiveException(Exception):
 
 
 DownloadEventHandler = Callable[["DownloadJob"], None]
-DownloadExceptionHandler = Callable[["DownloadJob", Optional[Exception]], None]
 
 
 @total_ordering
@@ -56,7 +55,6 @@ 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)")
 
@@ -72,11 +70,7 @@ 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[DownloadExceptionHandler] = PrivateAttr(default=None)
-
-    def __hash__(self) -> int:
-        """Return hash of the string representation of this object, for indexing."""
-        return hash(str(self))
+    _on_error: Optional[DownloadEventHandler] = PrivateAttr(default=None)
 
     def __le__(self, other: "DownloadJob") -> bool:
         """Return True if this job's priority is less than another's."""
@@ -93,26 +87,6 @@ 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."""
@@ -129,7 +103,7 @@ class DownloadJob(BaseModel):
         return self._on_complete
 
     @property
-    def on_error(self) -> Optional[DownloadExceptionHandler]:
+    def on_error(self) -> Optional[DownloadEventHandler]:
         """Return the on_error event handler."""
         return self._on_error
 
@@ -144,7 +118,7 @@ class DownloadJob(BaseModel):
         on_progress: Optional[DownloadEventHandler] = None,
         on_complete: Optional[DownloadEventHandler] = None,
         on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadExceptionHandler] = None,
+        on_error: Optional[DownloadEventHandler] = None,
     ) -> None:
         """Set the callbacks for download events."""
         self._on_start = on_start
@@ -176,10 +150,10 @@ class DownloadQueueServiceBase(ABC):
         on_progress: Optional[DownloadEventHandler] = None,
         on_complete: Optional[DownloadEventHandler] = None,
         on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadExceptionHandler] = None,
+        on_error: Optional[DownloadEventHandler] = None,
     ) -> DownloadJob:
         """
-        Create and enqueue download job.
+        Create a download job.
 
         :param source: Source of the download as a URL.
         :param dest: Path to download to. See below.
@@ -201,25 +175,6 @@ 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]:
         """
@@ -242,21 +197,21 @@ class DownloadQueueServiceBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_all_jobs(self) -> None:
+    def cancel_all_jobs(self):
         """Cancel all active and enquedjobs."""
         pass
 
     @abstractmethod
-    def prune_jobs(self) -> None:
+    def prune_jobs(self):
         """Prune completed and errored queue items from the job list."""
         pass
 
     @abstractmethod
-    def cancel_job(self, job: DownloadJob) -> None:
+    def cancel_job(self, job: DownloadJob):
         """Cancel the job, clearing partial downloads and putting it into ERROR state."""
         pass
 
     @abstractmethod
-    def join(self) -> None:
+    def join(self):
         """Wait until all jobs are off the queue."""
         pass

invokeai/app/services/download/download_default.py

@@ -5,9 +5,10 @@ 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
+from typing import Any, Dict, List, Optional, Set
 
 import requests
 from pydantic.networks import AnyHttpUrl
@@ -20,7 +21,6 @@ from invokeai.backend.util.logging import InvokeAILogger
 
 from .download_base import (
     DownloadEventHandler,
-    DownloadExceptionHandler,
     DownloadJob,
     DownloadJobCancelledException,
     DownloadJobStatus,
@@ -36,6 +36,18 @@ 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,
@@ -87,33 +99,6 @@ 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,
@@ -124,27 +109,32 @@ class DownloadQueueService(DownloadQueueServiceBase):
         on_progress: Optional[DownloadEventHandler] = None,
         on_complete: Optional[DownloadEventHandler] = None,
         on_cancelled: Optional[DownloadEventHandler] = None,
-        on_error: Optional[DownloadExceptionHandler] = None,
+        on_error: Optional[DownloadEventHandler] = None,
     ) -> DownloadJob:
-        """Create and enqueue a download job and return it."""
+        """Create a download job and return its ID."""
         if not self._accept_download_requests:
             raise ServiceInactiveException(
                 "The download service is not currently accepting requests. Please call start() to initialize the service."
             )
-        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,
-        )
+        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)
         return job
 
     def join(self) -> None:
@@ -160,7 +150,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
         with self._lock:
             to_delete = set()
             for job_id, job in self._jobs.items():
-                if job.in_terminal_state:
+                if self._in_terminal_state(job):
                     to_delete.add(job_id)
             for job_id in to_delete:
                 del self._jobs[job_id]
@@ -182,12 +172,19 @@ class DownloadQueueService(DownloadQueueServiceBase):
         with self._lock:
             job.cancel()
 
-    def cancel_all_jobs(self) -> None:
+    def cancel_all_jobs(self, preserve_partial: bool = False) -> None:
         """Cancel all jobs (those not in enqueued, running or paused state)."""
         for job in self._jobs.values():
-            if not job.in_terminal_state:
+            if not self._in_terminal_state(job):
                 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()
@@ -217,7 +214,7 @@ 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, excp)
+                self._signal_job_error(job)
             except DownloadJobCancelledException:
                 self._signal_job_cancelled(job)
                 self._cleanup_cancelled_job(job)
@@ -238,8 +235,6 @@ 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
 
@@ -301,7 +296,6 @@ 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:
@@ -328,9 +322,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
             try:
                 job.on_start(job)
             except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_start callback: {traceback.format_exception(e)}"
-                )
+                self._logger.error(e)
         if self._event_bus:
             assert job.download_path
             self._event_bus.emit_download_started(str(job.source), job.download_path.as_posix())
@@ -340,9 +332,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
             try:
                 job.on_progress(job)
             except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_progress callback: {traceback.format_exception(e)}"
-                )
+                self._logger.error(e)
         if self._event_bus:
             assert job.download_path
             self._event_bus.emit_download_progress(
@@ -358,9 +348,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
             try:
                 job.on_complete(job)
             except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_complete callback: {traceback.format_exception(e)}"
-                )
+                self._logger.error(e)
         if self._event_bus:
             assert job.download_path
             self._event_bus.emit_download_complete(
@@ -368,36 +356,29 @@ 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(
-                    f"An error occurred while processing the on_cancelled callback: {traceback.format_exception(e)}"
-                )
+                self._logger.error(e)
         if self._event_bus:
             self._event_bus.emit_download_cancelled(str(job.source))
 
-    def _signal_job_error(self, job: DownloadJob, excp: Optional[Exception] = None) -> None:
+    def _signal_job_error(self, job: DownloadJob) -> 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, excp)
+                job.on_error(job)
             except Exception as e:
-                self._logger.error(
-                    f"An error occurred while processing the on_error callback: {traceback.format_exception(e)}"
-                )
+                self._logger.error(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.debug(f"Cleaning up leftover files from cancelled download job {job.download_path}")
+        self._logger.warning(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, Dict, List, Optional, Union
+from typing import Any, Optional
 
 from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
 from invokeai.app.services.session_queue.session_queue_common import (
@@ -404,72 +404,53 @@ class EventServiceBase:
             },
         )
 
-    def emit_model_install_downloading(
-        self,
-        source: str,
-        local_path: str,
-        bytes: int,
-        total_bytes: int,
-        parts: List[Dict[str, Union[str, int]]],
-    ) -> None:
+    def emit_model_install_started(self, source: str) -> None:
         """
-        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.
+        Emitted when an install job is started.
 
         :param source: Source of the model; local path, repo_id or url
         """
         self.__emit_model_event(
-            event_name="model_install_running",
+            event_name="model_install_started",
             payload={"source": source},
         )
 
-    def emit_model_install_completed(self, source: str, key: str, total_bytes: Optional[int] = None) -> None:
+    def emit_model_install_completed(self, source: str, key: str) -> None:
         """
-        Emit when an install job is completed successfully.
+        Emitted 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_cancelled(self, source: str) -> None:
+    def emit_model_install_progress(
+        self,
+        source: str,
+        current_bytes: int,
+        total_bytes: int,
+    ) -> None:
         """
-        Emit when an install job is cancelled.
+        Emitted while the install job is in progress.
+        (Downloaded models only)
 
-        :param source: Source of the model; local path, repo_id or url
+        :param source: Source of the model
+        :param current_bytes: Number of bytes downloaded so far
+        :param total_bytes: Total bytes to download
         """
         self.__emit_model_event(
-            event_name="model_install_cancelled",
-            payload={"source": source},
+            event_name="model_install_progress",
+            payload={
+                "source": source,
+                "current_bytes": int,
+                "total_bytes": int,
+            },
         )
 
     def emit_model_install_error(
@@ -479,11 +460,10 @@ class EventServiceBase:
         error: str,
     ) -> None:
         """
-        Emit when an install job encounters an exception.
+        Emitted when an install job encounters an exception.
 
         :param source: Source of the model
-        :param error_type: The name of the exception
-        :param error: A text description of the exception
+        :param exception: The exception that raised the error
         """
         self.__emit_model_event(
             event_name="model_install_error",

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -132,6 +132,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             source_node_id=source_node_id,
                             result=outputs.model_dump(),
                         )
+                    self.__invoker.services.performance_statistics.log_stats()
 
                 except KeyboardInterrupt:
                     pass
@@ -194,7 +195,6 @@ 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

@@ -30,13 +30,23 @@ 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):
         """
@@ -67,8 +77,45 @@ class InvocationStatsServiceBase(ABC):
         pass
 
     @abstractmethod
-    def log_stats(self, graph_execution_state_id: str):
+    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):
         """
         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

@@ -1,84 +1,25 @@
-from collections import defaultdict
-from dataclasses import dataclass
+from dataclasses import dataclass, field
+from typing import Dict
+
+# size of GIG in bytes
+GIG = 1073741824
 
 
 @dataclass
-class NodeExecutionStats:
-    """Class for tracking execution stats of an invocation node."""
+class NodeStats:
+    """Class for tracking execution stats of an invocation node"""
 
-    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
+    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
 
 
-class GraphExecutionStats:
-    """Class for tracking execution stats of a graph."""
+@dataclass
+class NodeLog:
+    """Class for tracking node usage"""
 
-    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
+    # {node_type => NodeStats}
+    nodes: Dict[str, NodeStats] = field(default_factory=dict)

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -1,5 +1,5 @@
 import time
-from contextlib import contextmanager
+from typing import Dict
 
 import psutil
 import torch
@@ -7,119 +7,161 @@ 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 GraphExecutionStats, NodeExecutionStats
-
-# Size of 1GB in bytes.
-GB = 2**30
+from .invocation_stats_common import GIG, NodeLog, NodeStats
 
 
 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):
-        # 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] = {}
+        # {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
 
     def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
 
-    @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()
+    class StatsContext:
+        """Context manager for collecting statistics."""
 
-            # Prune stale stats. There should be none since we're starting a new graph, but just in case.
-            self._prune_stale_stats()
+        invocation: BaseInvocation
+        collector: "InvocationStatsServiceBase"
+        graph_id: str
+        start_time: float
+        ram_used: int
+        model_manager: ModelManagerServiceBase
 
-        # 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 __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
 
-        try:
-            # 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,
+        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,
             )
-            self._stats[graph_execution_state_id].add_node_execution_stats(node_stats)
 
-    def _prune_stale_stats(self):
-        """Check all graphs being tracked and prune any that have completed/errored.
+    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()
+            self._cache_stats[graph_execution_state_id] = CacheStats()
+        return self.StatsContext(invocation, graph_execution_state_id, self._invoker.services.model_manager, self)
 
-        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:
+    def reset_all_stats(self):
+        """Zero all statistics"""
+        self._stats = {}
+
+    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}")
+
+    def update_mem_stats(
+        self,
+        ram_used: float,
+        ram_changed: float,
+    ):
+        self.ram_used = ram_used
+        self.ram_changed = ram_changed
+
+    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():
             try:
-                graph_execution_state = self._invoker.services.graph_execution_manager.get(graph_execution_state_id)
+                current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
             except Exception:
-                # 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}.")
+                errored.add(graph_id)
                 continue
 
-            if not graph_execution_state.is_complete():
-                # The graph is still running, don't prune it.
+            if not current_graph_state.is_complete():
                 continue
 
-            to_prune.append(graph_execution_state_id)
+            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
 
-        for graph_execution_state_id in to_prune:
-            del self._stats[graph_execution_state_id]
-            del self._cache_stats[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
 
-        if len(to_prune) > 0:
-            logger.info(f"Pruned stale graph stats for {to_prune}.")
+            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")
 
-    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}.")
+            completed.add(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 completed:
+            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]
+        for graph_id in errored:
+            del self._stats[graph_id]
+            del self._cache_stats[graph_id]

invokeai/app/services/model_install/__init__.py

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

invokeai/app/services/model_install/model_install_base.py

@@ -1,42 +1,27 @@
-# 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, Set, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
-from pydantic import BaseModel, Field, PrivateAttr, field_validator
+from pydantic import BaseModel, Field, 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, ModelRepoVariant
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
+from invokeai.backend.model_manager import AnyModelConfig
 
 
 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):
@@ -89,31 +74,12 @@ 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, 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.
-    """
+    """A HuggingFace repo_id, with optional variant and sub-folder."""
 
     repo_id: str
-    variant: Optional[ModelRepoVariant] = ModelRepoVariant.FP16
-    subfolder: Optional[Path] = None
+    variant: Optional[str] = None
+    subfolder: Optional[str | Path] = None
     access_token: Optional[str] = None
     type: Literal["hf"] = "hf"
 
@@ -137,22 +103,19 @@ class URLModelSource(StringLikeSource):
 
     url: AnyHttpUrl
     access_token: Optional[str] = None
-    type: Literal["url"] = "url"
+    type: Literal["generic_url"] = "generic_url"
 
     def __str__(self) -> str:
         """Return string version of the url when string rep needed."""
         return str(self.url)
 
 
-ModelSource = Annotated[
-    Union[LocalModelSource, HFModelSource, CivitaiModelSource, URLModelSource], Field(discriminator="type")
-]
+ModelSource = Annotated[Union[LocalModelSource, HFModelSource, 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."
@@ -165,74 +128,15 @@ 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")
-    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)
+    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
 
     def set_error(self, e: Exception) -> None:
         """Record the error and traceback from an exception."""
-        self._exception = e
+        self.error_type = e.__class__.__name__
+        self.error = "".join(traceback.format_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."""
@@ -242,8 +146,6 @@ class ModelInstallServiceBase(ABC):
         self,
         app_config: InvokeAIAppConfig,
         record_store: ModelRecordServiceBase,
-        download_queue: DownloadQueueServiceBase,
-        metadata_store: ModelMetadataStore,
         event_bus: Optional["EventServiceBase"] = None,
     ):
         """
@@ -254,14 +156,12 @@ 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, invoker: Optional[Invoker] = None) -> None:
+    def start(self, *args: Any, **kwarg: Any) -> None:
         """Start the installer service."""
 
     @abstractmethod
-    def stop(self, invoker: Optional[Invoker] = None) -> None:
+    def stop(self, *args: Any, **kwarg: Any) -> None:
         """Stop the model install service. After this the objection can be safely deleted."""
 
     @property
@@ -364,13 +264,9 @@ class ModelInstallServiceBase(ABC):
         """
 
     @abstractmethod
-    def get_job_by_source(self, source: ModelSource) -> List[ModelInstallJob]:
+    def get_job(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
         """
@@ -382,19 +278,16 @@ class ModelInstallServiceBase(ABC):
         """Prune all completed and errored jobs."""
 
     @abstractmethod
-    def cancel_job(self, job: ModelInstallJob) -> None:
-        """Cancel the indicated job."""
-
-    @abstractmethod
-    def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:
+    def wait_for_installs(self) -> List[ModelInstallJob]:
         """
         Wait for all pending installs to complete.
 
         This will block until all pending installs have
-        completed, been cancelled, or errored out.
+        completed, been cancelled, or errored out. It will
+        block indefinitely if one or more jobs are in the
+        paused state.
 
-        :param timeout: Wait up to indicated number of seconds. Raise an Exception('timeout') if
-        installs do not complete within the indicated time.
+        It will return the current list of jobs.
         """
 
     @abstractmethod

invokeai/app/services/model_install/model_install_default.py

@@ -1,72 +1,60 @@
 """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 Empty, Queue
+from queue import 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.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.app.services.events import EventServiceBase
+from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase, UnknownModelException
 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,
 )
 
-TMPDIR_PREFIX = "tmpinstall_"
+# marker that the queue is done and that thread should exit
+STOP_JOB = ModelInstallJob(
+    source=LocalModelSource(path="stop"),
+    local_path=Path("/dev/null"),
+)
 
 
 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.
@@ -79,26 +67,10 @@ 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: 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)
+        self._install_jobs = []
+        self._install_queue = Queue()
+        self._cached_model_paths = set()
+        self._models_installed = set()
 
     @property
     def app_config(self) -> InvokeAIAppConfig:  # noqa D102
@@ -112,31 +84,69 @@ class ModelInstallService(ModelInstallServiceBase):
     def event_bus(self) -> Optional[EventServiceBase]:  # noqa D102
         return self._event_bus
 
-    # 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:
+    def start(self, *args: Any, **kwarg: Any) -> None:
         """Start the installer thread."""
-        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()
+        self._start_installer_thread()
+        self.sync_to_config()
 
-    def stop(self, invoker: Optional[Invoker] = None) -> None:
+    def stop(self, *args: Any, **kwarg: Any) -> None:
         """Stop the installer thread; after this the object can be deleted and garbage collected."""
-        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
+        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)
 
     def register_path(
         self,
@@ -162,12 +172,7 @@ 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
-        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_path = self._copy_model(model_path, dest_path)
         new_hash = FastModelHash.hash(new_path)
         assert new_hash == old_hash, f"{model_path}: Model hash changed during installation, possibly corrupted."
 
@@ -177,56 +182,43 @@ class ModelInstallService(ModelInstallServiceBase):
             info,
         )
 
-    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)}'")
+    def import_model(
+        self,
+        source: ModelSource,
+        config: Optional[Dict[str, Any]] = None,
+    ) -> ModelInstallJob:  # noqa D102
+        if not config:
+            config = {}
 
-        self._install_jobs.append(install_job)
-        return install_job
+        # 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")
 
     def list_jobs(self) -> List[ModelInstallJob]:  # noqa D102
         return self._install_jobs
 
-    def get_job_by_source(self, source: ModelSource) -> List[ModelInstallJob]:  # noqa D102
+    def get_job(self, source: ModelSource) -> List[ModelInstallJob]:  # noqa D102
         return [x for x in self._install_jobs if x.source == source]
 
-    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")
+    def wait_for_installs(self) -> List[ModelInstallJob]:  # noqa D102
         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 not x.in_terminal_state]
+        unfinished_jobs = [
+            x for x in self._install_jobs if x.status not in [InstallStatus.COMPLETED, InstallStatus.ERROR]
+        ]
         self._install_jobs = unfinished_jobs
 
     def sync_to_config(self) -> None:
@@ -242,108 +234,10 @@ 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.clear()
+        self._models_installed: Set[str] = set()
         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.
@@ -426,6 +320,28 @@ 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
@@ -481,279 +397,3 @@ 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

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

invokeai/app/services/model_records/model_records_base.py

@@ -4,15 +4,10 @@ Abstract base class for storing and retrieving model configuration records.
 """
 
 from abc import ABC, abstractmethod
-from enum import Enum
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple, Union
+from typing import List, Optional, 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):
@@ -31,33 +26,11 @@ 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[str, Any], AnyModelConfig]) -> AnyModelConfig:
+    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -81,7 +54,7 @@ class ModelRecordServiceBase(ABC):
         pass
 
     @abstractmethod
-    def update_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
+    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
         """
         Update the model, returning the updated version.
 
@@ -102,47 +75,6 @@ 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

@@ -42,11 +42,9 @@ Typical usage:
 
 import json
 import sqlite3
-from math import ceil
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Set, Tuple, Union
+from typing import List, Optional, Union
 
-from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
@@ -54,14 +52,11 @@ 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,
 )
 
@@ -69,6 +64,9 @@ 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.
@@ -80,12 +78,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         self._db = db
         self._cursor = self._db.conn.cursor()
 
-    @property
-    def db(self) -> SqliteDatabase:
-        """Return the underlying database."""
-        return self._db
-
-    def add_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
+    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -300,95 +293,3 @@ 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

@@ -6,7 +6,6 @@ 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
 
 
@@ -29,8 +28,7 @@ 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(app_config=config, logger=logger))
-    migrator.register_migration(build_migration_4())
+    migrator.register_migration(build_migration_3())
     migrator.run_migrations()
 
     return db

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

@@ -11,6 +11,8 @@ 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):
@@ -23,6 +25,8 @@ 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:
@@ -96,6 +100,45 @@ 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

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

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

@@ -1,83 +0,0 @@
-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

@@ -23,6 +23,7 @@ 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)
 
@@ -45,9 +46,10 @@ class MigrateModelYamlToDb1:
     logger: Logger
     cursor: sqlite3.Cursor
 
-    def __init__(self, config: InvokeAIAppConfig, logger: Logger, cursor: sqlite3.Cursor = None) -> None:
-        self.config = config
-        self.logger = logger
+    def __init__(self, cursor: sqlite3.Cursor = None) -> None:
+        self.config = InvokeAIAppConfig.get_config()
+        self.config.parse_args()
+        self.logger = InvokeAILogger.get_logger()
         self.cursor = cursor
 
     def get_yaml(self) -> DictConfig:

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

@@ -1,4 +1,5 @@
 {
+  "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",
@@ -76,12 +77,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 256,
       "position": {
         "x": 1250,
         "y": 1500
-      },
-      "width": 320,
-      "height": 219
+      }
     },
     {
       "id": "d8ace142-c05f-4f1d-8982-88dc7473958d",
@@ -147,12 +148,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 227,
       "position": {
         "x": 700,
         "y": 1375
-      },
-      "width": 320,
-      "height": 193
+      }
     },
     {
       "id": "771bdf6a-0813-4099-a5d8-921a138754d4",
@@ -213,12 +214,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 225,
       "position": {
         "x": 375,
         "y": 1900
-      },
-      "width": 320,
-      "height": 189
+      }
     },
     {
       "id": "f7564dd2-9539-47f2-ac13-190804461f4e",
@@ -314,12 +315,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 340,
       "position": {
         "x": 775,
         "y": 1900
-      },
-      "width": 320,
-      "height": 295
+      }
     },
     {
       "id": "1d887701-df21-4966-ae6e-a7d82307d7bd",
@@ -415,12 +416,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 340,
       "position": {
         "x": 1200,
         "y": 1900
-      },
-      "width": 320,
-      "height": 293
+      }
     },
     {
       "id": "ca1d020c-89a8-4958-880a-016d28775cfa",
@@ -433,7 +434,7 @@
         "notes": "",
         "isIntermediate": true,
         "useCache": true,
-        "version": "1.1.1",
+        "version": "1.1.0",
         "nodePack": "invokeai",
         "inputs": {
           "image": {
@@ -536,12 +537,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 511,
       "position": {
         "x": 1650,
         "y": 1900
-      },
-      "width": 320,
-      "height": 451
+      }
     },
     {
       "id": "f50624ce-82bf-41d0-bdf7-8aab11a80d48",
@@ -639,12 +640,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 32,
       "position": {
         "x": 1650,
         "y": 1775
-      },
-      "width": 320,
-      "height": 24
+      }
     },
     {
       "id": "c3737554-8d87-48ff-a6f8-e71d2867f434",
@@ -657,7 +658,7 @@
         "notes": "",
         "isIntermediate": true,
         "useCache": true,
-        "version": "1.5.1",
+        "version": "1.5.0",
         "nodePack": "invokeai",
         "inputs": {
           "positive_conditioning": {
@@ -865,12 +866,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 705,
       "position": {
         "x": 2128.740065979906,
         "y": 1232.6219060454753
-      },
-      "width": 320,
-      "height": 612
+      }
     },
     {
       "id": "3ed9b2ef-f4ec-40a7-94db-92e63b583ec0",
@@ -977,12 +978,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 267,
       "position": {
         "x": 2559.4751127537957,
         "y": 1246.6000376741406
-      },
-      "width": 320,
-      "height": 224
+      }
     },
     {
       "id": "5ca498a4-c8c8-4580-a396-0c984317205d",
@@ -1078,12 +1079,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 32,
       "position": {
         "x": 1650,
         "y": 1675
-      },
-      "width": 320,
-      "height": 24
+      }
     },
     {
       "id": "63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16b",
@@ -1136,12 +1137,12 @@
           }
         }
       },
+      "width": 320,
+      "height": 256,
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invokeai/app/services/workflow_records/default_workflows/Prompt from File.json

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

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invokeai/app/services/workflow_records/default_workflows/TextToImage_SD15.json

@@ -84,12 +84,12 @@
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invokeai/app/services/workflow_records/default_workflows/TextToImage_SDXL.json

@@ -80,12 +80,12 @@
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     },
     {
       "id": "reactflow__edge-3774ec24-a69e-4254-864c-097d07a6256fvalue-faf965a4-7530-427b-b1f3-4ba6505c2a08style",
-      "type": "default",
       "source": "3774ec24-a69e-4254-864c-097d07a6256f",
       "target": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+      "type": "default",
       "sourceHandle": "value",
       "targetHandle": "style"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/workflow_records_common.py

@@ -31,7 +31,6 @@ class WorkflowRecordOrderBy(str, Enum, metaclass=MetaEnum):
 class WorkflowCategory(str, Enum, metaclass=MetaEnum):
     User = "user"
     Default = "default"
-    Project = "project"
 
 
 class WorkflowMeta(BaseModel):

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -169,7 +169,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
 
             self._cursor.execute(count_query, count_params)
             total = self._cursor.fetchone()[0]
-            pages = total // per_page + (total % per_page > 0)
+            pages = int(total / per_page) + 1
 
             return PaginatedResults(
                 items=workflows,

invokeai/backend/image_util/depth_anything/__init__.py

@@ -1,109 +0,0 @@
-import pathlib
-from typing import Literal, Union
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-from einops import repeat
-from PIL import Image
-from torchvision.transforms import Compose
-
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
-from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
-from invokeai.backend.util.devices import choose_torch_device
-from invokeai.backend.util.util import download_with_progress_bar
-
-config = InvokeAIAppConfig.get_config()
-
-DEPTH_ANYTHING_MODELS = {
-    "large": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vitl14.pth",
-    },
-    "base": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vitb14.pth",
-    },
-    "small": {
-        "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
-        "local": "any/annotators/depth_anything/depth_anything_vits14.pth",
-    },
-}
-
-
-transform = Compose(
-    [
-        Resize(
-            width=518,
-            height=518,
-            resize_target=False,
-            keep_aspect_ratio=True,
-            ensure_multiple_of=14,
-            resize_method="lower_bound",
-            image_interpolation_method=cv2.INTER_CUBIC,
-        ),
-        NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        PrepareForNet(),
-    ]
-)
-
-
-class DepthAnythingDetector:
-    def __init__(self) -> None:
-        self.model = None
-        self.model_size: Union[Literal["large", "base", "small"], None] = None
-
-    def load_model(self, model_size=Literal["large", "base", "small"]):
-        DEPTH_ANYTHING_MODEL_PATH = pathlib.Path(config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"])
-        if not DEPTH_ANYTHING_MODEL_PATH.exists():
-            download_with_progress_bar(DEPTH_ANYTHING_MODELS[model_size]["url"], DEPTH_ANYTHING_MODEL_PATH)
-
-        if not self.model or model_size != self.model_size:
-            del self.model
-            self.model_size = model_size
-
-            match self.model_size:
-                case "small":
-                    self.model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
-                case "base":
-                    self.model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
-                case "large":
-                    self.model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
-                case _:
-                    raise TypeError("Not a supported model")
-
-            self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
-            self.model.eval()
-
-        self.model.to(choose_torch_device())
-        return self.model
-
-    def to(self, device):
-        self.model.to(device)
-        return self
-
-    def __call__(self, image, resolution=512, offload=False):
-        image = np.array(image, dtype=np.uint8)
-        image = image[:, :, ::-1] / 255.0
-
-        image_height, image_width = image.shape[:2]
-        image = transform({"image": image})["image"]
-        image = torch.from_numpy(image).unsqueeze(0).to(choose_torch_device())
-
-        with torch.no_grad():
-            depth = self.model(image)
-            depth = F.interpolate(depth[None], (image_height, image_width), mode="bilinear", align_corners=False)[0, 0]
-            depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
-
-        depth_map = repeat(depth, "h w -> h w 3").cpu().numpy().astype(np.uint8)
-        depth_map = Image.fromarray(depth_map)
-
-        new_height = int(image_height * (resolution / image_width))
-        depth_map = depth_map.resize((resolution, new_height))
-
-        if offload:
-            del self.model
-
-        return depth_map

invokeai/backend/image_util/depth_anything/model/blocks.py

@@ -1,145 +0,0 @@
-import torch.nn as nn
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    if len(in_shape) >= 4:
-        out_shape4 = out_shape
-
-    if expand:
-        out_shape1 = out_shape
-        out_shape2 = out_shape * 2
-        out_shape3 = out_shape * 4
-        if len(in_shape) >= 4:
-            out_shape4 = out_shape * 8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    if len(in_shape) >= 4:
-        scratch.layer4_rn = nn.Conv2d(
-            in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-        )
-
-    return scratch
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module."""
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups = 1
-
-        self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
-
-        self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
-
-        if self.bn:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn:
-            out = self.bn1(out)
-
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block."""
-
-    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups = 1
-
-        self.expand = expand
-        out_features = features
-        if self.expand:
-            out_features = features // 2
-
-        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
-        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-        self.size = size
-
-    def forward(self, *xs, size=None):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-
-        output = self.resConfUnit2(output)
-
-        if (size is None) and (self.size is None):
-            modifier = {"scale_factor": 2}
-        elif size is None:
-            modifier = {"size": self.size}
-        else:
-            modifier = {"size": size}
-
-        output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
-
-        output = self.out_conv(output)
-
-        return output

invokeai/backend/image_util/depth_anything/model/dpt.py

@@ -1,183 +0,0 @@
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .blocks import FeatureFusionBlock, _make_scratch
-
-torchhub_path = Path(__file__).parent.parent / "torchhub"
-
-
-def _make_fusion_block(features, use_bn, size=None):
-    return FeatureFusionBlock(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-        size=size,
-    )
-
-
-class DPTHead(nn.Module):
-    def __init__(self, nclass, in_channels, features, out_channels, use_bn=False, use_clstoken=False):
-        super(DPTHead, self).__init__()
-
-        self.nclass = nclass
-        self.use_clstoken = use_clstoken
-
-        self.projects = nn.ModuleList(
-            [
-                nn.Conv2d(
-                    in_channels=in_channels,
-                    out_channels=out_channel,
-                    kernel_size=1,
-                    stride=1,
-                    padding=0,
-                )
-                for out_channel in out_channels
-            ]
-        )
-
-        self.resize_layers = nn.ModuleList(
-            [
-                nn.ConvTranspose2d(
-                    in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
-                ),
-                nn.ConvTranspose2d(
-                    in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
-                ),
-                nn.Identity(),
-                nn.Conv2d(
-                    in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
-                ),
-            ]
-        )
-
-        if use_clstoken:
-            self.readout_projects = nn.ModuleList()
-            for _ in range(len(self.projects)):
-                self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()))
-
-        self.scratch = _make_scratch(
-            out_channels,
-            features,
-            groups=1,
-            expand=False,
-        )
-
-        self.scratch.stem_transpose = None
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        head_features_1 = features
-        head_features_2 = 32
-
-        if nclass > 1:
-            self.scratch.output_conv = nn.Sequential(
-                nn.Conv2d(head_features_1, head_features_1, kernel_size=3, stride=1, padding=1),
-                nn.ReLU(True),
-                nn.Conv2d(head_features_1, nclass, kernel_size=1, stride=1, padding=0),
-            )
-        else:
-            self.scratch.output_conv1 = nn.Conv2d(
-                head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1
-            )
-
-            self.scratch.output_conv2 = nn.Sequential(
-                nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
-                nn.ReLU(True),
-                nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
-                nn.ReLU(True),
-                nn.Identity(),
-            )
-
-    def forward(self, out_features, patch_h, patch_w):
-        out = []
-        for i, x in enumerate(out_features):
-            if self.use_clstoken:
-                x, cls_token = x[0], x[1]
-                readout = cls_token.unsqueeze(1).expand_as(x)
-                x = self.readout_projects[i](torch.cat((x, readout), -1))
-            else:
-                x = x[0]
-
-            x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
-
-            x = self.projects[i](x)
-            x = self.resize_layers[i](x)
-
-            out.append(x)
-
-        layer_1, layer_2, layer_3, layer_4 = out
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv1(path_1)
-        out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
-        out = self.scratch.output_conv2(out)
-
-        return out
-
-
-class DPT_DINOv2(nn.Module):
-    def __init__(
-        self,
-        features,
-        out_channels,
-        encoder="vitl",
-        use_bn=False,
-        use_clstoken=False,
-    ):
-        super(DPT_DINOv2, self).__init__()
-
-        assert encoder in ["vits", "vitb", "vitl"]
-
-        # # in case the Internet connection is not stable, please load the DINOv2 locally
-        # if use_local:
-        #     self.pretrained = torch.hub.load(
-        #         torchhub_path / "facebookresearch_dinov2_main",
-        #         "dinov2_{:}14".format(encoder),
-        #         source="local",
-        #         pretrained=False,
-        #     )
-        # else:
-        #     self.pretrained = torch.hub.load(
-        #         "facebookresearch/dinov2",
-        #         "dinov2_{:}14".format(encoder),
-        #     )
-
-        self.pretrained = torch.hub.load(
-            "facebookresearch/dinov2",
-            "dinov2_{:}14".format(encoder),
-        )
-
-        dim = self.pretrained.blocks[0].attn.qkv.in_features
-
-        self.depth_head = DPTHead(1, dim, features, out_channels=out_channels, use_bn=use_bn, use_clstoken=use_clstoken)
-
-    def forward(self, x):
-        h, w = x.shape[-2:]
-
-        features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True)
-
-        patch_h, patch_w = h // 14, w // 14
-
-        depth = self.depth_head(features, patch_h, patch_w)
-        depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
-        depth = F.relu(depth)
-
-        return depth.squeeze(1)

invokeai/backend/image_util/depth_anything/utilities/util.py

@@ -1,227 +0,0 @@
-import math
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method)
-
-    sample["disparity"] = cv2.resize(sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST)
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height)."""
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller
-                    than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
-            new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
-            new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
-
-            if "semseg_mask" in sample:
-                # sample["semseg_mask"] = cv2.resize(
-                #     sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
-                # )
-                sample["semseg_mask"] = F.interpolate(
-                    torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode="nearest"
-                ).numpy()[0, 0]
-
-            if "mask" in sample:
-                sample["mask"] = cv2.resize(
-                    sample["mask"].astype(np.float32),
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-                # sample["mask"] = sample["mask"].astype(bool)
-
-        # print(sample['image'].shape, sample['depth'].shape)
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std."""
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input."""
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        if "semseg_mask" in sample:
-            sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
-            sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
-
-        return sample

invokeai/backend/install/model_install_backend.py

@@ -283,16 +283,11 @@ 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:
-                models_to_remove.append(path)
-
-        for path in models_to_remove:
-            logger.warning(f"{path} already installed. Skipping")
-            model_list.remove(path)
+                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/detect_baked_in_vae.py

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

invokeai/backend/model_management/lora.py

@@ -13,7 +13,6 @@ from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.model_management.model_load_optimizations import skip_torch_weight_init
 
 from .models.lora import LoRAModel
 
@@ -212,12 +211,8 @@ class ModelPatcher:
                 for i in range(ti_embedding.shape[0]):
                     new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
-            # Modify text_encoder.
-            # resize_token_embeddings(...) constructs a new torch.nn.Embedding internally. Initializing the weights of
-            # this embedding is slow and unnecessary, so we wrap this step in skip_torch_weight_init() to save some
-            # time.
-            with skip_torch_weight_init():
-                text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
+            # modify text_encoder
+            text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
             model_embeddings = text_encoder.get_input_embeddings()
 
             for ti_name, ti in ti_list:

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.
         """
@@ -781,9 +781,6 @@ 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_management/model_probe.py

@@ -370,8 +370,6 @@ class LoRACheckpointProbe(CheckpointProbeBase):
             return BaseModelType.StableDiffusion1
         elif token_vector_length == 1024:
             return BaseModelType.StableDiffusion2
-        elif token_vector_length == 1280:
-            return BaseModelType.StableDiffusionXL  # recognizes format at https://civitai.com/models/224641
         elif token_vector_length == 2048:
             return BaseModelType.StableDiffusionXL
         else:

invokeai/backend/model_management/models/sdxl.py

@@ -1,16 +1,11 @@
 import json
 import os
 from enum import Enum
-from pathlib import Path
 from typing import Literal, Optional
 
 from omegaconf import OmegaConf
 from pydantic import Field
 
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_management.detect_baked_in_vae import has_baked_in_sdxl_vae
-from invokeai.backend.util.logging import InvokeAILogger
-
 from .base import (
     BaseModelType,
     DiffusersModel,
@@ -121,28 +116,14 @@ class StableDiffusionXLModel(DiffusersModel):
         # The convert script adapted from the diffusers package uses
         # strings for the base model type. To avoid making too many
         # source code changes, we simply translate here
-        if Path(output_path).exists():
-            return output_path
-
         if isinstance(config, cls.CheckpointConfig):
             from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache
 
-            # Hack in VAE-fp16 fix - If model sdxl-vae-fp16-fix is installed,
-            # then we bake it into the converted model unless there is already
-            # a nonstandard VAE installed.
-            kwargs = {}
-            app_config = InvokeAIAppConfig.get_config()
-            vae_path = app_config.models_path / "sdxl/vae/sdxl-vae-fp16-fix"
-            if vae_path.exists() and not has_baked_in_sdxl_vae(Path(model_path)):
-                InvokeAILogger.get_logger().warning("No baked-in VAE detected. Inserting sdxl-vae-fp16-fix.")
-                kwargs["vae_path"] = vae_path
-
             return _convert_ckpt_and_cache(
                 version=base_model,
                 model_config=config,
                 output_path=output_path,
                 use_safetensors=False,  # corrupts sdxl models for some reason
-                **kwargs,
             )
         else:
             return model_path

invokeai/backend/model_manager/__init__.py

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

invokeai/backend/model_manager/config.py

@@ -99,17 +99,6 @@ 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

@@ -1,50 +0,0 @@
-"""
-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

@@ -1,21 +0,0 @@
-"""
-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

@@ -1,187 +0,0 @@
-# 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

@@ -1,61 +0,0 @@
-# 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

@@ -1,92 +0,0 @@
-# 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

@@ -1,202 +0,0 @@
-# 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

@@ -1,221 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
-"""
-SQL Storage for Model Metadata
-"""
-
-import sqlite3
-from typing import List, Optional, Set, Tuple
-
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
-
-from .fetch import ModelMetadataFetchBase
-from .metadata_base import AnyModelRepoMetadata, UnknownMetadataException
-
-
-class ModelMetadataStore:
-    """Store, search and fetch model metadata retrieved from remote repositories."""
-
-    def __init__(self, db: SqliteDatabase):
-        """
-        Initialize a new object from preexisting sqlite3 connection and threading lock objects.
-
-        :param conn: sqlite3 connection object
-        :param lock: threading Lock object
-        """
-        super().__init__()
-        self._db = db
-        self._cursor = self._db.conn.cursor()
-
-    def add_metadata(self, model_key: str, metadata: AnyModelRepoMetadata) -> None:
-        """
-        Add a block of repo metadata to a model record.
-
-        The model record config must already exist in the database with the
-        same key. Otherwise a FOREIGN KEY constraint exception will be raised.
-
-        :param model_key: Existing model key in the `model_config` table
-        :param metadata: ModelRepoMetadata object to store
-        """
-        json_serialized = metadata.model_dump_json()
-        with self._db.lock:
-            try:
-                self._cursor.execute(
-                    """--sql
-                    INSERT INTO model_metadata(
-                       id,
-                       metadata
-                    )
-                    VALUES (?,?);
-                    """,
-                    (
-                        model_key,
-                        json_serialized,
-                    ),
-                )
-                self._update_tags(model_key, metadata.tags)
-                self._db.conn.commit()
-            except sqlite3.IntegrityError as excp:  # FOREIGN KEY error: the key was not in model_config table
-                self._db.conn.rollback()
-                raise UnknownMetadataException from excp
-            except sqlite3.Error as excp:
-                self._db.conn.rollback()
-                raise excp
-
-    def get_metadata(self, model_key: str) -> AnyModelRepoMetadata:
-        """Retrieve the ModelRepoMetadata corresponding to model key."""
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT metadata FROM model_metadata
-                WHERE id=?;
-                """,
-                (model_key,),
-            )
-            rows = self._cursor.fetchone()
-            if not rows:
-                raise UnknownMetadataException("model metadata not found")
-            return ModelMetadataFetchBase.from_json(rows[0])
-
-    def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:  # key, metadata
-        """Dump out all the metadata."""
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT id,metadata FROM model_metadata;
-                """,
-                (),
-            )
-            rows = self._cursor.fetchall()
-        return [(x[0], ModelMetadataFetchBase.from_json(x[1])) for x in rows]
-
-    def update_metadata(self, model_key: str, metadata: AnyModelRepoMetadata) -> AnyModelRepoMetadata:
-        """
-        Update metadata corresponding to the model with the indicated key.
-
-        :param model_key: Existing model key in the `model_config` table
-        :param metadata: ModelRepoMetadata object to update
-        """
-        json_serialized = metadata.model_dump_json()  # turn it into a json string.
-        with self._db.lock:
-            try:
-                self._cursor.execute(
-                    """--sql
-                    UPDATE model_metadata
-                    SET
-                        metadata=?
-                    WHERE id=?;
-                    """,
-                    (json_serialized, model_key),
-                )
-                if self._cursor.rowcount == 0:
-                    raise UnknownMetadataException("model metadata not found")
-                self._update_tags(model_key, metadata.tags)
-                self._db.conn.commit()
-            except sqlite3.Error as e:
-                self._db.conn.rollback()
-                raise e
-
-        return self.get_metadata(model_key)
-
-    def list_tags(self) -> Set[str]:
-        """Return all tags in the tags table."""
-        self._cursor.execute(
-            """--sql
-            select tag_text from tags;
-            """
-        )
-        return {x[0] for x in self._cursor.fetchall()}
-
-    def search_by_tag(self, tags: Set[str]) -> Set[str]:
-        """Return the keys of models containing all of the listed tags."""
-        with self._db.lock:
-            try:
-                matches: Optional[Set[str]] = None
-                for tag in tags:
-                    self._cursor.execute(
-                        """--sql
-                        SELECT a.model_id FROM model_tags AS a,
-                                                     tags AS b
-                        WHERE a.tag_id=b.tag_id
-                          AND b.tag_text=?;
-                        """,
-                        (tag,),
-                    )
-                    model_keys = {x[0] for x in self._cursor.fetchall()}
-                    if matches is None:
-                        matches = model_keys
-                    matches = matches.intersection(model_keys)
-            except sqlite3.Error as e:
-                raise e
-        return matches if matches else set()
-
-    def search_by_author(self, author: str) -> Set[str]:
-        """Return the keys of models authored by the indicated author."""
-        self._cursor.execute(
-            """--sql
-            SELECT id FROM model_metadata
-            WHERE author=?;
-            """,
-            (author,),
-        )
-        return {x[0] for x in self._cursor.fetchall()}
-
-    def search_by_name(self, name: str) -> Set[str]:
-        """
-        Return the keys of models with the indicated name.
-
-        Note that this is the name of the model given to it by
-        the remote source. The user may have changed the local
-        name. The local name will be located in the model config
-        record object.
-        """
-        self._cursor.execute(
-            """--sql
-            SELECT id FROM model_metadata
-            WHERE name=?;
-            """,
-            (name,),
-        )
-        return {x[0] for x in self._cursor.fetchall()}
-
-    def _update_tags(self, model_key: str, tags: Set[str]) -> None:
-        """Update tags for the model referenced by model_key."""
-        # remove previous tags from this model
-        self._cursor.execute(
-            """--sql
-            DELETE FROM model_tags
-            WHERE model_id=?;
-            """,
-            (model_key,),
-        )
-
-        for tag in tags:
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO tags (
-                  tag_text
-                  )
-                VALUES (?);
-                """,
-                (tag,),
-            )
-            self._cursor.execute(
-                """--sql
-                SELECT tag_id
-                FROM tags
-                WHERE tag_text = ?
-                LIMIT 1;
-                """,
-                (tag,),
-            )
-            tag_id = self._cursor.fetchone()[0]
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO model_tags (
-                   model_id,
-                   tag_id
-                  )
-                VALUES (?,?);
-                """,
-                (model_key, tag_id),
-            )

invokeai/backend/model_manager/probe.py

@@ -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.get("prediction_type", "epsilon") == "v_prediction":
+        if scheduler_conf["prediction_type"] == "v_prediction":
             return SchedulerPredictionType.VPrediction
-        elif scheduler_conf.get("prediction_type", "epsilon") == "epsilon":
+        elif scheduler_conf["prediction_type"] == "epsilon":
             return SchedulerPredictionType.Epsilon
         else:
             raise InvalidModelConfigException("Unknown scheduler prediction type: {scheduler_conf['prediction_type']}")

invokeai/backend/model_manager/util/select_hf_files.py

@@ -1,132 +0,0 @@
-# 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

@@ -34,23 +34,18 @@ 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):
-            if config.precision == "bfloat16":
-                return "bfloat16"
-            else:
-                return "float16"
+            return "float16"
     elif device.type == "mps":
         return "float16"
     return "float32"
 
 
 def torch_dtype(device: torch.device) -> torch.dtype:
-    precision = choose_precision(device)
-    if precision == "float16":
+    if config.full_precision:
+        return torch.float32
+    if choose_precision(device) == "float16":
         return torch.float16
-    if precision == "bfloat16":
-        return torch.bfloat16
     else:
-        # "auto", "autocast", "float32"
         return torch.float32
 
 

invokeai/frontend/install/invokeai_update.py

@@ -68,9 +68,12 @@ def welcome(latest_release: str, latest_prerelease: str):
         yield ""
         yield "This script will update InvokeAI to the latest release, or to the development version of your choice."
         yield ""
+        yield "When updating to an arbitrary tag or branch, be aware that the front end may be mismatched to the backend,"
+        yield "making the web frontend unusable. Please downgrade to the latest release if this happens."
+        yield ""
         yield "[bold yellow]Options:"
         yield f"""[1] Update to the latest [bold]official release[/bold] ([italic]{latest_release}[/italic])
-[2] Update to the latest [bold]pre-release[/bold] (may be buggy, database backups are recommended before installation; caveat emptor!) ([italic]{latest_prerelease}[/italic])
+[2] Update to the latest [bold]pre-release[/bold] (may be buggy; caveat emptor!) ([italic]{latest_prerelease}[/italic])
 [3] Manually enter the [bold]version[/bold] you wish to update to"""
 
     console.rule()

invokeai/frontend/web/.eslintignore

@@ -7,4 +7,4 @@ stats.html
 index.html
 .yarn/
 *.scss
-src/services/api/schema.ts
+src/services/api/schema.d.ts

invokeai/frontend/web/.eslintrc.js

@@ -28,16 +28,12 @@ module.exports = {
     'i18next',
     'path',
     'unused-imports',
-    'simple-import-sort',
-    'eslint-plugin-import',
-    // These rules are too strict for normal usage, but are useful for optimizing rerenders
-    // '@arthurgeron/react-usememo',
   ],
   root: true,
   rules: {
     'path/no-relative-imports': ['error', { maxDepth: 0 }],
     curly: 'error',
-    'i18next/no-literal-string': 'warn',
+    'i18next/no-literal-string': 2,
     'react/jsx-no-bind': ['error', { allowBind: true }],
     'react/jsx-curly-brace-presence': [
       'error',
@@ -47,7 +43,6 @@ module.exports = {
     'no-var': 'error',
     'brace-style': 'error',
     'prefer-template': 'error',
-    'import/no-duplicates': 'error',
     radix: 'error',
     'space-before-blocks': 'error',
     'import/prefer-default-export': 'off',
@@ -62,18 +57,6 @@ module.exports = {
         argsIgnorePattern: '^_',
       },
     ],
-    // These rules are too strict for normal usage, but are useful for optimizing rerenders
-    // '@arthurgeron/react-usememo/require-usememo': [
-    //   'warn',
-    //   {
-    //     strict: false,
-    //     checkHookReturnObject: false,
-    //     fix: { addImports: true },
-    //     checkHookCalls: false,
-
-    //   },
-    // ],
-    // '@arthurgeron/react-usememo/require-memo': 'warn',
     '@typescript-eslint/ban-ts-comment': 'warn',
     '@typescript-eslint/no-explicit-any': 'warn',
     '@typescript-eslint/no-empty-interface': [
@@ -82,47 +65,7 @@ module.exports = {
         allowSingleExtends: true,
       },
     ],
-    '@typescript-eslint/consistent-type-imports': [
-      'error',
-      {
-        prefer: 'type-imports',
-        fixStyle: 'separate-type-imports',
-        disallowTypeAnnotations: true,
-      },
-    ],
-    '@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: [
-    {
-      files: ['*.stories.tsx'],
-      rules: {
-        'i18next/no-literal-string': 'off',
-      },
-    },
-  ],
   settings: {
     react: {
       version: 'detect',