* added HrfScale type with initial value
* working
* working
* working
* working
* working
* added addHrfToGraph
* continueing to implement this
* working on this
* comments
* working
* made hrf into its own collapse
* working on adding strength slider
* working
* working
* refactoring
* working
* change of this working: 0
* removed onnx support since apparently its not used
* working
* made scale integer
* trying out psycicpebbles idea
* working
* working on this
* working
* added toggle
* comments
* self review
* fixing things
* remove 'any' type
* fixing typing
* changed initial strength value to 3 (large values cause issues)
* set denoising start to be 1 - strength to resemble image to image
* set initial value
* added image to image
* pr1
* pr2
* updating to resolution finding
* working
* working
* working
* working
* working
* working
* working
* working
* working
* use memo
* connect rescale hw to noise
* working
* fixed min bug
* nit
* hides elements conditionally
* style
* feat(ui): add config for HRF, disable if feature disabled or ONNX model in use
* fix(ui): use `useCallback` for HRF toggle
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* 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>
The canvas needs to be set to staging mode as soon as a canvas-destined batch is enqueued. If the batch is is fully canceled before an image is generated, we need to remove that batch from the canvas `batchIds` watchlist, else canvas gets stuck in staging mode with no way to exit.
The changes here allow the batch status to be tracked, and if a batch has all its items completed, we can remove it from the `batchIds` watchlist. The `batchIds` watchlist now accurately represents *incomplete* canvas batches, fixing this cause of soft lock.
- Update backend metadata for t2i adapter
- Fix typo in `T2IAdapterInvocation`: `ip_adapter_model` -> `t2i_adapter_model`
- Update linear graphs to use t2i adapter
- Add client metadata recall for t2i adapter
- Fix bug with controlnet metadata recall - processor should be set to 'none' when recalling a control adapter
Control adapters logic/state/ui is now generalized to hold controlnet, ip_adapter and t2i_adapter. In the future, other control adapter types can be added.
TODO:
- Limit IP adapter to 1
- Add T2I adapter to linear graphs
- Fix autoprocess
- T2I metadata saving & recall
- Improve on control adapters UI
This caused a crapload of network requests any time an image was generated.
The counts are necessary to handle the logic for inserting images into existing image list caches; we have to keep track of the counts.
Replace tag invalidation with manual cache updates in all cases, except the initial request (which is necessary to get the initial image counts).
One subtle change is to make the counts an object instead of a number. This is required for `immer` to handle draft states. This should be raised as a bug with RTK Query, as no error is thrown when attempting to update a primitive immer draft.
* feat(ui): max upscale pixels config
Add `maxUpscalePixels: number` to the app config. The number should be the *total* number of pixels eg `maxUpscalePixels: 4096 * 4096`.
If not provided, any size image may be upscaled.
If the config is provided, users will see be advised if their image is too large for either model, or told to switch to an x2 model if it's only too large for x4.
The message is via tooltip in the popover and via toast if the user uses the hotkey to upscale.
* feat(ui): "mayUpscale" -> "isAllowedToUpscale"
* add control net to useRecallParams
* got recall controlnets working
* fix metadata viewer controlnet
* fix type errors
* fix controlnet metadata viewer
* add ip adapter to metadata
* added ip adapter to recall parameters
* got ip adapter recall working, still need to fix type errors
* fix type issues
* clean up logs
* python formatting
* cleanup
* fix(ui): only store `image_name` as ip adapter image
* fix(ui): use nullish coalescing operator for numbers
Need to use the nullish coalescing operator `??` instead of false-y coalescing operator `||` when the value being check is a number. This prevents unintended coalescing when the value is zero and therefore false-y.
* feat(ui): fall back on default values for ip adapter metadata
* fix(ui): remove unused schema
* feat(ui): re-use existing schemas in metadata schema
* fix(ui): do not disable invocationCache
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
This hook was rerendering any time anything changed. Moved it to a logical component, put its useEffects inside the component. This reduces the effect of the rerenders to just that tiny always-null component.
* feat(ui): add error handling for enqueueBatch route, remove sessions
This re-implements the handling for the session create/invoke errors, but for batches.
Also remove all references to the old sessions routes in the UI.
* feat(ui): improve canvas image error UI
* make canvas error state gray instead of red
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
This is actually a platform-specific issue. `madge` is complaining about a circular dependency on a single file - `invokeai/frontend/web/src/features/queue/store/nanoStores.ts`. In that file, we import from the `nanostores` package. Very similar name to the file itself.
The error only appears on Windows and macOS, I imagine because those systems both resolve `nanostores` to itself before resolving to the package.
The solution is simple - rename `nanoStores.ts`. It's now `queueNanoStore.ts`.
- No longer need to make network request to add image to board after it's finished - removed
- Update linear graphs & upscale graph to save image to the board
- Update autoSwitch logic so when image is generated we still switch to the right board
* break out separate functions for preselected images, remove recallAllParameters dep as it causes circular logic with model being set
* 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
* feat(ui): tweak queue UI components
* fix(ui): manually dispatch queue status query on queue item status change
RTK Query occasionally aborts the query that occurs when the tag is invalidated, especially if multples of them fire in rapid succession.
This resulted in the queue status and progress bar sometimes not reseting when the queue finishes its last item.
Manually dispatch the query now to get around this. Eventually should probably move this to a socket so we don't need to keep responding to socket with HTTP requests. Just send ti directly via socket
* chore(ui): remove errant console.logs
* fix(ui): do not accumulate node outputs in outputs area
* fix(ui): fix merge issue
---------
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Add `batch_id` to outbound events. This necessitates adding it to both `InvocationContext` and `InvocationQueueItem`. This allows the canvas to receive images.
When the user enqueues a batch on the canvas, it is expected that all images from that batch are directed to the canvas.
The simplest, most flexible solution is to add the `batch_id` to the invocation context-y stuff. Then everything knows what batch it came from, and we can have the canvas pick up images associated with its list of canvas `batch_id`s.
* 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>
The immutable and serializable checks for redux can cause substantial performance issues. The immutable check in particular is pretty heavy. It's only run in dev mode, but this and really slow down the already-slower performance of dev mode.
The most important one for us is serializable, which has far less of a performance impact.
The immutable check is largely redundant because we use immer-backed RTK for everything and immer gives us confidence there.
Disable the immutable check, leaving serializable in.
JSX is not serializable, so it cannot be in redux. Non-serializable global state may be put into `nanostores`.
- Use `nanostores` for `customStarUI`
- Use `nanostores` for `headerComponent`
- Re-enable the serializable & immutable check redux middlewares
This simply hides nodes from the workflow editor. The nodes will still work if an API request is made with them. For example, you could hide `iterate` nodes from the workflow editor, but if the Linear UI makes use of those nodes, they will still function.
- Update `AppConfig` with optional property `nodesDenylist: string[]`
- If provided, nodes are filtered out by `type` in the workflow editor
