- Refactor how metadata is handled to support a user-defined metadata in graphs
- Update workflow embed handling
- Update UI to work with these changes
- Update tests to support metadata/workflow changes
This fixes a weird issue where the list images method needed to handle `None` for its `limit` and `offset` arguments, in order to get a count of all intermediates.
Upgrade pydantic and fastapi to latest.
- pydantic~=2.4.2
- fastapi~=103.2
- fastapi-events~=0.9.1
**Big Changes**
There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes.
**Invocations**
The biggest change relates to invocation creation, instantiation and validation.
Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie.
Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`.
With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation.
This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method.
In the end, this implementation is cleaner.
**Invocation Fields**
In pydantic v2, you can no longer directly add or remove fields from a model.
Previously, we did this to add the `type` field to invocations.
**Invocation Decorators**
With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper.
A similar technique is used for `invocation_output()`.
**Minor Changes**
There are a number of minor changes around the pydantic v2 models API.
**Protected `model_` Namespace**
All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_".
Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple.
```py
class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
```
**Model Serialization**
Pydantic models no longer have `Model.dict()` or `Model.json()`.
Instead, we use `Model.model_dump()` or `Model.model_dump_json()`.
**Model Deserialization**
Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions.
Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model.
```py
adapter_graph = TypeAdapter(Graph)
deserialized_graph_from_json = adapter_graph.validate_json(graph_json)
deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict)
```
**Field Customisation**
Pydantic `Field`s no longer accept arbitrary args.
Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field.
**Schema Customisation**
FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec.
This necessitates two changes:
- Our schema customization logic has been revised
- Schema parsing to build node templates has been revised
The specific aren't important, but this does present additional surface area for bugs.
**Performance Improvements**
Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node.
I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
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
* UI for bulk downloading boards or groups of images
* placeholder route for bulk downloads that does nothing
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
- New routes to clear, enable, disable and get the status of the cache
- Status includes hits, misses, size, max size, enabled
- Add client cache queries and mutations, abstracted into hooks
- Add invocation cache status area (next to queue status) w/ buttons
* fix(config): fix typing issues in `config/`
`config/invokeai_config.py`:
- use `Optional` for things that are optional
- fix typing of `ram_cache_size()` and `vram_cache_size()`
- remove unused and incorrectly typed method `autoconvert_path`
- fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere
`config/base.py`:
- use `cls` for first arg of class methods
- use `Optional` for things that are optional
- fix minor type issue related to setting of `env_prefix`
- remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`)
* feat: queued generation and batches
Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes.
* chore: flake8, isort, black
* fix(nodes): fix incorrect service stop() method
* fix(nodes): improve names of a few variables
* fix(tests): fix up tests after changes to batches/queue
* feat(tests): add unit tests for session queue helper functions
* feat(ui): dynamic prompts is always enabled
* feat(queue): add queue_status_changed event
* feat(ui): wip queue graphs
* feat(nodes): move cleanup til after invoker startup
* feat(nodes): add cancel_by_batch_ids
* feat(ui): wip batch graphs & UI
* fix(nodes): remove `Batch.batch_id` from required
* fix(ui): cleanup and use fixedCacheKey for all mutations
* fix(ui): remove orphaned nodes from canvas graphs
* fix(nodes): fix cancel_by_batch_ids result count
* fix(ui): only show cancel batch tooltip when batches were canceled
* chore: isort
* fix(api): return `[""]` when dynamic prompts generates no prompts
Just a simple fallback so we always have a prompt.
* feat(ui): dynamicPrompts.combinatorial is always on
There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so.
* feat: add queue_id & support logic
* feat(ui): fix upscale button
It prepends the upscale operation to queue
* feat(nodes): return queue item when enqueuing a single graph
This facilitates one-off graph async workflows in the client.
* feat(ui): move controlnet autoprocess to queue
* fix(ui): fix non-serializable DOMRect in redux state
* feat(ui): QueueTable performance tweaks
* feat(ui): update queue list
Queue items expand to show the full queue item. Just as JSON for now.
* wip threaded session_processor
* feat(nodes,ui): fully migrate queue to session_processor
* feat(nodes,ui): add processor events
* feat(ui): ui tweaks
* feat(nodes,ui): consolidate events, reduce network requests
* feat(ui): cleanup & abstract queue hooks
* feat(nodes): optimize batch permutation
Use a generator to do only as much work as is needed.
Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory.
The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory.
* feat(ui): add seed behaviour parameter
This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration:
- Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion
- Per prompt: Use a different seed for every single prompt
"Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation.
* fix(ui): remove extraneous random seed nodes from linear graphs
* fix(ui): fix controlnet autoprocess not working when queue is running
* feat(queue): add timestamps to queue status updates
Also show execution time in queue list
* feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem
This allows for much simpler handling of queue items.
* feat(api): deprecate sessions router
* chore(backend): tidy logging in `dependencies.py`
* fix(backend): respect `use_memory_db`
* feat(backend): add `config.log_sql` (enables sql trace logging)
* feat: add invocation cache
Supersedes #4574
The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned.
## Results
This feature provides anywhere some significant to massive performance improvement.
The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal.
## Overview
A new `invocation_cache` service is added to handle the caching. There's not much to it.
All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching.
The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic.
To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key.
## In-Memory Implementation
An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts.
Max node cache size is added as `node_cache_size` under the `Generation` config category.
It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher.
Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them.
## Node Definition
The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`.
Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`.
The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something.
## One Gotcha
Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again.
If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit.
## Linear UI
The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs.
This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default.
This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`.
## Workflow Editor
All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user.
The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes.
Users should consider saving their workflows after loading them in and having them updated.
## Future Enhancements - Callback
A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not.
This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field.
## Future Enhancements - Persisted Cache
Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future.
* fix(ui): fix queue list item width
* feat(nodes): do not send the whole node on every generator progress
* feat(ui): strip out old logic related to sessions
Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed...
* feat(ui): fix up param collapse labels
* feat(ui): click queue count to go to queue tab
* tidy(queue): update comment, query format
* feat(ui): fix progress bar when canceling
* fix(ui): fix circular dependency
* feat(nodes): bail on node caching logic if `node_cache_size == 0`
* feat(nodes): handle KeyError on node cache pop
* feat(nodes): bypass cache codepath if caches is disabled
more better no do thing
* fix(ui): reset api cache on connect/disconnect
* feat(ui): prevent enqueue when no prompts generated
* feat(ui): add queue controls to workflow editor
* feat(ui): update floating buttons & other incidental UI tweaks
* fix(ui): fix missing/incorrect translation keys
* fix(tests): add config service to mock invocation services
invoking needs access to `node_cache_size` to occur
* optionally remove pause/resume buttons from queue UI
* option to disable prepending
* chore(ui): remove unused file
* feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
- also implement pessimistic updates for starring, only changing the images that were successfully updated by backend
- some autoformat changes crept in
multi-select actions include:
- drag to board to move all to that board
- right click to add all to board or delete all
backend changes:
- add routes for changing board for list of image names, deleting list of images
- change image-specific routes to `images/i/{image_name}` to not clobber other routes (like `images/upload`, `images/delete`)
- subclass pydantic `BaseModel` as `BaseModelExcludeNull`, which excludes null values when calling `dict()` on the model. this fixes inconsistent types related to JSON parsing null values into `null` instead of `undefined`
- remove `board_id` from `remove_image_from_board`
frontend changes:
- multi-selection stuff uses `ImageDTO[]` as payloads, for dnd and other mutations. this gives us access to image `board_id`s when hitting routes, and enables efficient cache updates.
- consolidate change board and delete image modals to handle single and multiples
- board totals are now re-fetched on mutation and not kept in sync manually - was way too tedious to do this
- fixed warning about nested `<p>` elements
- closes#4088 , need to handle case when `autoAddBoardId` is `"none"`
- add option to show gallery image delete button on every gallery image
frontend refactors/organisation:
- make typegen script js instead of ts
- enable `noUncheckedIndexedAccess` to help avoid bugs when indexing into arrays, many small changes needed to satisfy TS after this
- move all image-related endpoints into `endpoints/images.ts`, its a big file now, but this fixes a number of circular dependency issues that were otherwise felt impossible to resolve
* feat(ui): enhance clear intermediates feature
- retrieve the # of intermediates using a new query (just uses list images endpoint w/ limit of 0)
- display the count in the UI
- add types for clearIntermediates mutation
- minor styling and verbiage changes
* feat(ui): remove unused settings option for guides
* feat(ui): use solid badge variant
consistent with the rest of the usage of badges
* feat(ui): update board ctx menu, add board auto-add
- add context menu to system boards - only open is select board. did this so that you dont think its broken when you click it
- add auto-add board. you can right click a user board to enable it for auto-add, or use the gallery settings popover to select it. the invoke button has a tooltip on a short delay to remind you that you have auto-add enabled
- made useBoardName hook, provide it a board id and it gets your the board name
- removed `boardIdToAdTo` state & logic, updated workflows to auto-switch and auto-add on image generation
* fix(ui): clear controlnet when clearing intermediates
* feat: Make Add Board icon a button
* feat(db, api): clear intermediates now clears all of them
* feat(ui): make reset webui text subtext style
* feat(ui): board name change submits on blur
---------
Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
* feat(ui): migrate listImages to RTK query using createEntityAdapter
- see comments in `endpoints/images.ts` for explanation of the caching
- so far, only manually updating `all` images when new image is generated. no other manual cache updates are implemented, but will be needed.
- fixed some weirdness with loading state components (like the spinners in gallery)
- added `useThumbnailFallback` for `IAIDndImage`, this displays the tiny webp thumbnail while the full-size images load
- comment out some old thunk related stuff in gallerySlice, which is no longer needed
* feat(ui): add manual cache updates for board changes (wip)
- update RTK Query caches when adding/removing single image to/from board
- work more on migrating all image-related operations to RTK Query
* update AddImagesToBoardContext so that it works when user uses context menu + modal
* handle case where no image is selected
* get assets working for main list and boards - dnd only
* feat(ui): migrate image uploads to RTK Query
- minor refactor of `ImageUploader` and `useImageUploadButton` hooks, simplify some logic
- style filesystem upload overlay to match existing UI
- replace all old `imageUploaded` thunks with `uploadImage` RTK Query calls, update associated logic including canvas related uploads
- simplify `PostUploadAction`s that only need to display user input
* feat(ui): remove `receivedPageOfImages` thunks
* feat(ui): remove `receivedImageUrls` thunk
* feat(ui): finish removing all images thunks
stuff now broken:
- image usage
- delete board images
- on first load, no image selected
* feat(ui): simplify `updateImage` cache manipulation
- we don't actually ever change categories, so we can remove a lot of logic
* feat(ui): simplify canvas autosave
- instead of using a network request to set the canvas generation as not intermediate, we can just do that in the graph
* feat(ui): simplify & handle edge cases in cache updates
* feat(db, api): support `board_id='none'` for `get_many` images queries
This allows us to get all images that are not on a board.
* chore(ui): regen types
* feat(ui): add `All Assets`, `No Board` boards
Restructure boards:
- `all images` is all images
- `all assets` is all assets
- `no board` is all images/assets without a board set
- user boards may have images and assets
Update caching logic
- much simpler without every board having sub-views of images and assets
- update drag and drop operations for all possible interactions
* chore(ui): regen types
* feat(ui): move download to top of context menu
* feat(ui): improve drop overlay styles
* fix(ui): fix image not selected on first load
- listen for first load of all images board, then select the first image
* feat(ui): refactor board deletion
api changes:
- add route to list all image names for a board. this is required to handle board + image deletion. we need to know every image in the board to determine the image usage across the app. this is fetched only when the delete board and images modal is opened so it's as efficient as it can be.
- update the delete board route to respond with a list of deleted `board_images` and `images`, as image names. this is needed to perform accurate clientside state & cache updates after deleting.
db changes:
- remove unused `board_images` service method to get paginated images dtos for a board. this is now done thru the list images endpoint & images service. needs a small logic change on `images.delete_images_on_board`
ui changes:
- simplify the delete board modal - no context, just minor prop drilling. this is feasible for boards only because the components that need to trigger and manipulate the modal are very close together in the tree
- add cache updates for `deleteBoard` & `deleteBoardAndImages` mutations
- the only thing we cannot do directly is on `deleteBoardAndImages`, update the `No Board` board. we'd need to insert image dtos that we may not have loaded. instead, i am just invalidating the tags for that `listImages` cache. so when you `deleteBoardAndImages`, the `No Board` will re-fetch the initial image limit. i think this is more efficient than e.g. fetching all image dtos to insert then inserting them.
- handle image usage for `deleteBoardAndImages`
- update all (i think/hope) the little bits and pieces in the UI to accomodate these changes
* fix(ui): fix board selection logic
* feat(ui): add delete board modal loading state
* fix(ui): use thumbnails for board cover images
* fix(ui): fix race condition with board selection
when selecting a board that doesn't have any images loaded, we need to wait until the images haveloaded before selecting the first image.
this logic is debounced to ~1000ms.
* feat(ui): name 'No Board' correctly, change icon
* fix(ui): do not cache listAllImageNames query
if we cache it, we can end up with stale image usage during deletion.
we could of course manually update the cache as we are doing elsewhere. but because this is a relatively infrequent network request, i'd like to trade increased cache mgmt complexity here for increased resource usage.
* feat(ui): reduce drag preview opacity, remove border
* fix(ui): fix incorrect queryArg used in `deleteImage` and `updateImage` cache updates
* fix(ui): fix doubled open in new tab
* fix(ui): fix new generations not getting added to 'No Board'
* fix(ui): fix board id not changing on new image when autosave enabled
* fix(ui): context menu when selection is 0
need to revise how context menu is triggered later, when we approach multi select
* fix(ui): fix deleting does not update counts for all images and all assets
* fix(ui): fix all assets board name in boards list collapse button
* fix(ui): ensure we never go under 0 for total board count
* fix(ui): fix text overflow on board names
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
* new route to clear intermediates
* UI to clear intermediates from settings modal
* cleanup
* PR feedback
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
1. add a /sync route for synchronizing the in-memory model lists to
models.yaml, the models directory, and the autoimport directories.
2. add optional destination_directories to convert_model and merge_model
operations.
3. add /ckpt_confs route for retrieving known legacy checkpoint configuration
files.
4. add /search route for finding all models in a directory located in the server
filesystem
Metadata for the Linear UI is now sneakily provided via a `MetadataAccumulator` node, which the client populates / hooks up while building the graph.
Additionally, we provide the unexpanded graph with the metadata API response.
Both of these are embedded into the PNGs.
- Remove `metadata` from `ImageDTO`
- Split up the `images/` routes to accomodate this; metadata is only retrieved per-image
- `images/{image_name}` now gets the DTO
- `images/{image_name}/metadata` gets the new metadata
- `images/{image_name}/full` gets the full-sized image file
- Remove old metadata service
- Add `MetadataAccumulator` node, `CoreMetadataField`, hook up to `LatentsToImage` node
- Add `get_raw()` method to `ItemStorage`, retrieves the row from DB as a string, no pydantic parsing
- Update `images`related services to handle storing and retrieving the new metadata
- Add `get_metadata_graph_from_raw_session` which extracts the `graph` from `session` without needing to hydrate the session in pydantic, in preparation for providing it as metadata; also removes all references to the `MetadataAccumulator` node
Image files are immutable and we expect deletion to result in no further requests for a given image, so we can set the max-age to something thicc.
Resolves#3426
The list models route should just be the base route path, and should use query parameters as opposed to path parameters (which cannot be optional)
Removed defaults for update model route - for the purposes of the API, we should always be explicit with this
- remove `image_origin` from most places where we interact with images
- consolidate image file storage into a single `images/` dir
Images have an `image_origin` attribute but it is not actually used when retrieving images, nor will it ever be. It is still used when creating images and helps to differentiate between internally generated images and uploads.
It was included in eg API routes and image service methods as a holdover from the previous app implementation where images were not managed in a database. Now that we have images in a db, we can do away with this and simplify basically everything that touches images.
The one potentially controversial change is to no longer separate internal and external images on disk. If we retain this separation, we have to keep `image_origin` around in a number of spots and it getting image paths on disk painful.
So, I am have gotten rid of this organisation. Images are now all stored in `images`, regardless of their origin. As we improve the image management features, this change will hopefully become transparent.
Because we dynamically insert images into the DB and UI's images state, `page`/`per_page` pagination makes loading the images awkward.
Using `offset`/`limit` pagination lets us query for images with an offset equal to the number of images already loaded (which match the query parameters).
The result is that we always get the correct next page of images when loading more.
- Remove `ImageType` entirely, it is confusing
- Create `ResourceOrigin`, may be `internal` or `external`
- Revamp `ImageCategory`, may be `general`, `mask`, `control`, `user`, `other`. Expect to add more as time goes on
- Update images `list` route to accept `include_categories` OR `exclude_categories` query parameters to afford finer-grained querying. All services are updated to accomodate this change.
The new setup should account for our types of images, including the combinations we couldn't really handle until now:
- Canvas init and masks
- Canvas when saved-to-gallery or merged
- `ImageType` is now restricted to `results` and `uploads`.
- Add a reserved `meta` field to nodes to hold the `is_intermediate` boolean. We can extend it in the future to support other node `meta`.
- Add a `is_intermediate` column to the `images` table to hold this. (When `latents`, `conditioning` etc are added to the DB, they will also have this column.)
- All nodes default to `*not* intermediate`. Nodes must explicitly be marked `intermediate` for their outputs to be `intermediate`.
- When building a graph, you can set `node.meta.is_intermediate=True` and it will be handled as an intermediate.
- Add a new `update()` method to the `ImageService`, and a route to call it. Updates have a strict model, currently only `session_id` and `image_category` may be updated.
- Add a new `update()` method to the `ImageRecordStorageService` to update the image record using the model.
When returning a `FileResponse`, we must provide a valid path, else an exception is raised outside the route handler.
Add the `validate_path` method back to the service so we can validate paths before returning the file.
I don't like this but apparently this is just how `starlette` and `fastapi` works with `FileResponse`.
- Address database feedback:
- Remove all the extraneous tables. Only an `images` table now:
- `image_type` and `image_category` are unrestricted strings. When creating images, the provided values are checked to ensure they are a valid type and category.
- Add `updated_at` and `deleted_at` columns. `deleted_at` is currently unused.
- Use SQLite's built-in timestamp features to populate these. Add a trigger to update `updated_at` when the row is updated. Currently no way to update a row.
- Rename the `id` column in `images` to `image_name`
- Rename `ImageCategory.IMAGE` to `ImageCategory.GENERAL`
- Move all exceptions outside their base classes to make them more portable.
- Add `width` and `height` columns to the database. These store the actual dimensions of the image file, whereas the metadata's `width` and `height` refer to the respective generation parameters and are nullable.
- Make `deserialize_image_record` take a `dict` instead of `sqlite3.Row`
- Improve comments throughout
- Tidy up unused code/files and some minor organisation
feat(nodes): add ResultsServiceABC & SqliteResultsService
**Doesn't actually work bc of circular imports. Can't even test it.**
- add a base class for ResultsService and SQLite implementation
- use `graph_execution_manager` `on_changed` callback to keep `results` table in sync
fix(nodes): fix results service bugs
chore(ui): regen api
fix(ui): fix type guards
feat(nodes): add `result_type` to results table, fix types
fix(nodes): do not shadow `list` builtin
feat(nodes): add results router
It doesn't work due to circular imports still
fix(nodes): Result class should use outputs classes, not fields
feat(ui): crude results router
fix(ui): send to canvas in currentimagebuttons not working
feat(nodes): add core metadata builder
feat(nodes): add design doc
feat(nodes): wip latents db stuff
feat(nodes): images_db_service and resources router
feat(nodes): wip images db & router
feat(nodes): update image related names
feat(nodes): update urlservice
feat(nodes): add high-level images service
The `ModelsList` OpenAPI schema is generated as being keyed by plain strings. This means that API consumers do not know the shape of the dict. It _should_ be keyed by the `SDModelType` enum.
Unfortunately, `fastapi` does not actually handle this correctly yet; it still generates the schema with plain string keys.
Adding this anyways though in hopes that it will be resolved upstream and we can get the correct schema. Until then, I'll implement the (simple but annoying) logic on the frontend.
https://github.com/pydantic/pydantic/issues/4393
