- Add `OnNodeError` and `OnNonFatalProcessorError` callbacks
- Move all session/node callbacks to `SessionRunner` - this ensures we dump perf stats before resetting them and generally makes sense to me
- Remove `complete` event from `SessionRunner`, it's essentially the same as `OnAfterRunSession`
- Remove extraneous `next_invocation` block, which would treat a processor error as a node error
- Simplify loops
- Add some callbacks for testing, to be removed before merge
- Metadata is merged with the config. We can simplify the MM substantially and remove the handling for metadata.
- Per discussion, we don't have an ETA for frontend implementation of tags, and with the realization that the tags from CivitAI are largely useless, there's no reason to keep tags in the MM right now. When we are ready to implement tags on the frontend, we can refer back to the implementation here and use it if it supports the design.
- Fix all tests.
Consolidate graph processing logic into session processor.
With graphs as the unit of work, and the session queue distributing graphs, we no longer need the invocation queue or processor.
Instead, the session processor dequeues the next session and processes it in a simple loop, greatly simplifying the app.
- Remove `graph_execution_manager` service.
- Remove `queue` (invocation queue) service.
- Remove `processor` (invocation processor) service.
- Remove queue-related logic from `Invoker`. It now only starts and stops the services, providing them with access to other services.
- Remove unused `invocation_retrieval_error` and `session_retrieval_error` events, these are no longer needed.
- Clean up stats service now that it is less coupled to the rest of the app.
- Refactor cancellation logic - cancellations now originate from session queue (i.e. HTTP cancel endpoint) and are emitted as events. Processor gets the events and sets the canceled event. Access to this event is provided to the invocation context for e.g. the step callback.
- Remove `sessions` router; it provided access to `graph_executions` but that no longer exists.
- ModelMetadataStoreService is now injected into ModelRecordStoreService
(these two services are really joined at the hip, and should someday be merged)
- ModelRecordStoreService is now injected into ModelManagerService
- Reduced timeout value for the various installer and download wait*() methods
- Introduced a Mock modelmanager for testing
- Removed bare print() statement with _logger in the install helper backend.
- Removed unused code from model loader init file
- Made `locker` a private variable in the `LoadedModel` object.
- Fixed up model merge frontend (will be deprecated anyway!)
- Replace legacy model manager service with the v2 manager.
- Update invocations to use new load interface.
- Fixed many but not all type checking errors in the invocations. Most
were unrelated to model manager
- Updated routes. All the new routes live under the route tag
`model_manager_v2`. To avoid confusion with the old routes,
they have the URL prefix `/api/v2/models`. The old routes
have been de-registered.
- Added a pytest for the loader.
- Updated documentation in contributing/MODEL_MANAGER.md
Replace `delete_on_startup: bool` & associated logic with `ephemeral: bool` and `TemporaryDirectory`.
The temp dir is created inside of `output_dir`. For example, if `output_dir` is `invokeai/outputs/tensors/`, then the temp dir might be `invokeai/outputs/tensors/tmpvj35ht7b/`.
The temp dir is cleaned up when the service is stopped, or when it is GC'd if not properly stopped.
In the event of a catastrophic crash where the temp files are not cleaned up, the user can delete the tempdir themselves.
This situation may not occur in normal use, but if you kill the process, python cannot clean up the temp dir itself. This includes running the app in a debugger and killing the debugger process - something I do relatively often.
Tests updated.
- The default is to not delete on startup - feels safer.
- The two services using this class _do_ delete on startup.
- The class has "ephemeral" removed from its name.
- Tests & app updated for this change.
Turns out they are just different enough in purpose that the implementations would be rather unintuitive. I've made a separate ObjectSerializer service to handle tensors and conditioning.
Refined the class a bit too.
Turns out `ItemStorageABC` was almost identical to `PickleStorageBase`. Instead of maintaining separate classes, we can use `ItemStorageABC` for both.
There's only one change needed - the `ItemStorageABC.set` method must return the newly stored item's ID. This allows us to let the service handle the responsibility of naming the item, but still create the requisite output objects during node execution.
The naming implementation is improved here. It extracts the name of the generic and appends a UUID to that string when saving items.
- New generic class `PickleStorageBase`, implements the same API as `LatentsStorageBase`, use for storing non-serializable data via pickling
- Implementation `PickleStorageTorch` uses `torch.save` and `torch.load`, same as `LatentsStorageDisk`
- Add `tensors: PickleStorageBase[torch.Tensor]` to `InvocationServices`
- Add `conditioning: PickleStorageBase[ConditioningFieldData]` to `InvocationServices`
- Remove `latents` service and all `LatentsStorage` classes
- Update `InvocationContext` and all usage of old `latents` service to use the new services/context wrapper methods
* add basic functionality for model metadata fetching from hf and civitai
* add storage
* start unit tests
* add unit tests and documentation
* add missing dependency for pytests
* remove redundant fetch; add modified/published dates; updated docs
* add code to select diffusers files based on the variant type
* implement Civitai installs
* make huggingface parallel downloading work
* add unit tests for model installation manager
- Fixed race condition on selection of download destination path
- Add fixtures common to several model_manager_2 unit tests
- Added dummy model files for testing diffusers and safetensors downloading/probing
- Refactored code for selecting proper variant from list of huggingface repo files
- Regrouped ordering of methods in model_install_default.py
* improve Civitai model downloading
- Provide a better error message when Civitai requires an access token (doesn't give a 403 forbidden, but redirects
to the HTML of an authorization page -- arrgh)
- Handle case of Civitai providing a primary download link plus additional links for VAEs, config files, etc
* add routes for retrieving metadata and tags
* code tidying and documentation
* fix ruff errors
* add file needed to maintain test root diretory in repo for unit tests
* fix self->cls in classmethod
* add pydantic plugin for mypy
* use TestSession instead of requests.Session to prevent any internet activity
improve logging
fix error message formatting
fix logging again
fix forward vs reverse slash issue in Windows install tests
* Several fixes of problems detected during PR review:
- Implement cancel_model_install_job and get_model_install_job routes
to allow for better control of model download and install.
- Fix thread deadlock that occurred after cancelling an install.
- Remove unneeded pytest_plugins section from tests/conftest.py
- Remove unused _in_terminal_state() from model_install_default.
- Remove outdated documentation from several spots.
- Add workaround for Civitai API results which don't return correct
URL for the default model.
* fix docs and tests to match get_job_by_source() rather than get_job()
* Update invokeai/backend/model_manager/metadata/fetch/huggingface.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Call CivitaiMetadata.model_validate_json() directly
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Second round of revisions suggested by @ryanjdick:
- Fix type mismatch in `list_all_metadata()` route.
- Do not have a default value for the model install job id
- Remove static class variable declarations from non Pydantic classes
- Change `id` field to `model_id` for the sqlite3 `model_tags` table.
- Changed AFTER DELETE triggers to ON DELETE CASCADE for the metadata and tags tables.
- Made the `id` field of the `model_metadata` table into a primary key to achieve uniqueness.
* Code cleanup suggested in PR review:
- Narrowed the declaration of the `parts` attribute of the download progress event
- Removed auto-conversion of str to Url in Url-containing sources
- Fixed handling of `InvalidModelConfigException`
- Made unknown sources raise `NotImplementedError` rather than `Exception`
- Improved status reporting on cached HuggingFace access tokens
* Multiple fixes:
- `job.total_size` returns a valid size for locally installed models
- new route `list_models` returns a paged summary of model, name,
description, tags and other essential info
- fix a few type errors
* consolidated all invokeai root pytest fixtures into a single location
* Update invokeai/backend/model_manager/metadata/metadata_store.py
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* Small tweaks in response to review comments:
- Remove flake8 configuration from pyproject.toml
- Use `id` rather than `modelId` for huggingface `ModelInfo` object
- Use `last_modified` rather than `LastModified` for huggingface `ModelInfo` object
- Add `sha256` field to file metadata downloaded from huggingface
- Add `Invoker` argument to the model installer `start()` and `stop()` routines
(but made it optional in order to facilitate use of the service outside the API)
- Removed redundant `PRAGMA foreign_keys` from metadata store initialization code.
* Additional tweaks and minor bug fixes
- Fix calculation of aggregate diffusers model size to only count the
size of files, not files + directories (which gives different unit test
results on different filesystems).
- Refactor _get_metadata() and _get_download_urls() to have distinct code paths
for Civitai, HuggingFace and URL sources.
- Forward the `inplace` flag from the source to the job and added unit test for this.
- Attach cached model metadata to the job rather than to the model install service.
* fix unit test that was breaking on windows due to CR/LF changing size of test json files
* fix ruff formatting
* a few last minor fixes before merging:
- Turn job `error` and `error_type` into properties derived from the exception.
- Add TODO comment about the reason for handling temporary directory destruction
manually rather than using tempfile.tmpdir().
* add unit tests for reporting HTTP download errors
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* add base definition of download manager
* basic functionality working
* add unit tests for download queue
* add documentation and FastAPI route
* fix docs
* add missing test dependency; fix import ordering
* fix file path length checking on windows
* fix ruff check error
* move release() into the __del__ method
* disable testing of stderr messages due to issues with pytest capsys fixture
* fix unsorted imports
* harmonized implementation of start() and stop() calls in download and & install modules
* Update invokeai/app/services/download/download_base.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* replace test datadir fixture with tmp_path
* replace DownloadJobBase->DownloadJob in download manager documentation
* make source and dest arguments to download_queue.download() an AnyHttpURL and Path respectively
* fix pydantic typecheck errors in the download unit test
* ruff formatting
* add "job cancelled" as an event rather than an exception
* fix ruff errors
* Update invokeai/app/services/download/download_default.py
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* use threading.Event to stop service worker threads; handle unfinished job edge cases
* remove dangling STOP job definition
* fix ruff complaint
* fix ruff check again
* avoid race condition when start() and stop() are called simultaneously from different threads
* avoid race condition in stop() when a job becomes active while shutting down
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
The graph library occasionally causes issues when the default graph changes substantially between versions and pydantic validation fails. See #5289 for an example.
We are not currently using the graph library, so we can disable it until we are ready to use it. It's possible that the workflow library will supersede it anyways.
- use simpler pattern for migration dependencies
- move SqliteDatabase & migration to utility method `init_db`, use this in both the app and tests, ensuring the same db schema is used in both
Simplifies a couple things:
- Init is more straightforward
- It's clear in the migrator that the connection we are working with is related to the SqliteDatabase
- Simplify init args to path (None means use memory), logger, and verbose
- Add docstrings to SqliteDatabase (it had almost none)
- Update all usages of the class
* chore: bump pydantic to 2.5.2
This release fixespydantic/pydantic#8175 and allows us to use `JsonValue`
* fix(ui): exclude public/en.json from prettier config
* fix(workflow_records): fix SQLite workflow insertion to ignore duplicates
* feat(backend): update workflows handling
Update workflows handling for Workflow Library.
**Updated Workflow Storage**
"Embedded Workflows" are workflows associated with images, and are now only stored in the image files. "Library Workflows" are not associated with images, and are stored only in DB.
This works out nicely. We have always saved workflows to files, but recently began saving them to the DB in addition to in image files. When that happened, we stopped reading workflows from files, so all the workflows that only existed in images were inaccessible. With this change, access to those workflows is restored, and no workflows are lost.
**Updated Workflow Handling in Nodes**
Prior to this change, workflows were embedded in images by passing the whole workflow JSON to a special workflow field on a node. In the node's `invoke()` function, the node was able to access this workflow and save it with the image. This (inaccurately) models workflows as a property of an image and is rather awkward technically.
A workflow is now a property of a batch/session queue item. It is available in the InvocationContext and therefore available to all nodes during `invoke()`.
**Database Migrations**
Added a `SQLiteMigrator` class to handle database migrations. Migrations were needed to accomodate the DB-related changes in this PR. See the code for details.
The `images`, `workflows` and `session_queue` tables required migrations for this PR, and are using the new migrator. Other tables/services are still creating tables themselves. A followup PR will adapt them to use the migrator.
**Other/Support Changes**
- Add a `has_workflow` column to `images` table to indicate that the image has an embedded workflow.
- Add handling for retrieving the workflow from an image in python. The image file must be fetched, the workflow extracted, and then sent to client, avoiding needing the browser to parse the image file. With the `has_workflow` column, the UI knows if there is a workflow to be fetched, and only fetches when the user requests to load the workflow.
- Add route to get the workflow from an image
- Add CRUD service/routes for the library workflows
- `workflow_images` table and services removed (no longer needed now that embedded workflows are not in the DB)
* feat(ui): updated workflow handling (WIP)
Clientside updates for the backend workflow changes.
Includes roughed-out workflow library UI.
* feat: revert SQLiteMigrator class
Will pursue this in a separate PR.
* feat(nodes): do not overwrite custom node module names
Use a different, simpler method to detect if a node is custom.
* feat(nodes): restore WithWorkflow as no-op class
This class is deprecated and no longer needed. Set its workflow attr value to None (meaning it is now a no-op), and issue a warning when an invocation subclasses it.
* fix(nodes): fix get_workflow from queue item dict func
* feat(backend): add WorkflowRecordListItemDTO
This is the id, name, description, created at and updated at workflow columns/attrs. Used to display lists of workflowsl
* chore(ui): typegen
* feat(ui): add workflow loading, deleting to workflow library UI
* feat(ui): workflow library pagination button styles
* wip
* feat: workflow library WIP
- Save to library
- Duplicate
- Filter/sort
- UI/queries
* feat: workflow library - system graphs - wip
* feat(backend): sync system workflows to db
* fix: merge conflicts
* feat: simplify default workflows
- Rename "system" -> "default"
- Simplify syncing logic
- Update UI to match
* feat(workflows): update default workflows
- Update TextToImage_SD15
- Add TextToImage_SDXL
- Add README
* feat(ui): refine workflow list UI
* fix(workflow_records): typo
* fix(tests): fix tests
* feat(ui): clean up workflow library hooks
* fix(db): fix mis-ordered db cleanup step
It was happening before pruning queue items - should happen afterwards, else you have to restart the app again to free disk space made available by the pruning.
* feat(ui): tweak reset workflow editor translations
* feat(ui): split out workflow redux state
The `nodes` slice is a rather complicated slice. Removing `workflow` makes it a bit more reasonable.
Also helps to flatten state out a bit.
* docs: update default workflows README
* fix: tidy up unused files, unrelated changes
* fix(backend): revert unrelated service organisational changes
* feat(backend): workflow_records.get_many arg "filter_text" -> "query"
* feat(ui): use custom hook in current image buttons
Already in use elsewhere, forgot to use it here.
* fix(ui): remove commented out property
* fix(ui): fix workflow loading
- Different handling for loading from library vs external
- Fix bug where only nodes and edges loaded
* fix(ui): fix save/save-as workflow naming
* fix(ui): fix circular dependency
* fix(db): fix bug with releasing without lock in db.clean()
* fix(db): remove extraneous lock
* chore: bump ruff
* fix(workflow_records): default `category` to `WorkflowCategory.User`
This allows old workflows to validate when reading them from the db or image files.
* hide workflow library buttons if feature is disabled
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Refactor services folder/module structure.
**Motivation**
While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward.
**Services**
Services are now in their own folder with a few files:
- `services/{service_name}/__init__.py`: init as needed, mostly empty now
- `services/{service_name}/{service_name}_base.py`: the base class for the service
- `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory`
- `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc
Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename.
There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`.
**Shared**
Things that are used across disparate services are in `services/shared/`:
- `default_graphs.py`: previously in `services/`
- `graphs.py`: previously in `services/`
- `paginatation`: generic pagination models used in a few services
- `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
**Service Dependencies**
Services that depend on other services now access those services via the `Invoker` object. This object is provided to the service as a kwarg to its `start()` method.
Until now, most services did not utilize this feature, and several services required their dependencies to be initialized and passed in on init.
Additionally, _all_ services are now registered as invocation services - including the low-level services. This obviates issues with inter-dependent services we would otherwise experience as we add workflow storage.
**Database Access**
Previously, we were passing in a separate sqlite connection and corresponding lock as args to services in their init. A good amount of posturing was done in each service that uses the db.
These objects, along with the sqlite startup and cleanup logic, is now abstracted into a simple `SqliteDatabase` class. This creates the shared connection and lock objects, enables foreign keys, and provides a `clean()` method to do startup db maintenance.
This is not a service as it's only used by sqlite services.
