- Bump `@invoke-ai/ui` for updated styles
- Update regex to parse prompts with newlines
- Update styling of overlay button when prompt has an error
- Fix bug where loading and error state sometimes weren't cleared
* feat(ui): get rid of convoluted socket vs appSocket redux actions
There's no need to have `socket...` and `appSocket...` actions.
I did this initially due to a misunderstanding about the sequence of handling from middleware to reducers.
* feat(ui): bump deps
Mainly bumping to get latest `redux-remember`.
A change to socket.io required a change to the types in `useSocketIO`.
* chore(ui): format
* feat(ui): add error handling to redux persistence layer
- Add an error handler to `redux-remember` config using our logger
- Add custom errors representing storage set and get failures
- Update storage driver to raise these accordingly
- wrap method to clear idbkeyval storage and tidy its logic up
* feat(ui): add debuggingLoggerMiddleware
This simply logs every action and a diff of the state change.
Due to the noise this creates, it's not added by default at all. Add it to the middlewares if you want to use it.
* feat(ui): add $socket to window if in dev mode
* fix(ui): do not enable cancel hotkeys on inputs
* fix(ui): use JSON.stringify for ROARR logger serializer
A recent change to ROARR introduced limits to the size of data that will logged. This ends up making our logs far less useful. Change the serializer back to what it was previously.
* feat(ui): change diff util, update debuggerLoggerMiddleware
The previous diff library would present deleted things as `undefined`. Unfortunately, a JSON.stringify cycle will strip those values out. The ROARR logger does this and so the diffs end up being a lot less useful, not showing removed keys.
The new diff library uses a different format for the delta that serializes nicely.
* feat(ui): add migrations to redux persistence layer
- All persisted slices must now have a slice config, consisting of their initial state and a migrate callback. The migrate callback is very simple for now, with no type safety. It adds missing properties to the state. A future enhancement might be to model the each slice's state with e.g. zod and have proper validation and types.
- Persisted slices now have a `_version` property
- The migrate callback is called inside `redux-remember`'s `unserialize` handler. I couldn't figure out a good way to put this into the reducer and do logging (reducers should have no side effects). Also I ran into a weird race condition that I couldn't figure out. And finally, the typings are tricky. This works for now.
- `generationSlice` and `canvasSlice` both need migrations for the new aspect ratio setup, this has been added
- Stuff related to persistence has been moved in to `store.ts` for simplicity
* feat(ui): clean up StorageError class
* fix(ui): scale method default is now 'auto'
* feat(ui): when changing controlnet model, enable autoconfig
* fix(ui): make embedding popover immediately accessible
Prevents hotkeys from being captured when embeddings are still loading.
Centralize the initial/min/max/etc values for all numerical params. We used this for some but at some point stopped updating it.
All numerical params now use their respective configs. Far fewer hardcoded values throughout the app now.
Also updated the config types a bit to better accommodate slider vs number input constraints.
Ensure workflow editor model selector component gets a value
This introduced some funky type issues related to ONNX models. ONNX doesn't work anyways (unmaintained). Instead of fixing the types to work with a non-working feature, ONNX is now removed entirely from the UI.
- Remove all refs to ONNX (and Olives)
- Fix some type issues
- Add ONNX nodes to the nodes denylist (so they are not visible in UI)
- Update VAE graph helper, which still had some ONNX logic. It's a very simple change and doesn't change any logic. Just removes some conditions that were for ONNX. I tested it and nothing broke.
- Regenerate types
- Fix prettier and eslint ignores for generated types
- Lint
- Prompt must have an open curly brace followed by a close curly brace to enable dynamic prompts processing
- If a the given prompt already had a dynamic prompt cached, do not re-process
- If processing is not needed, user may invoke immediately
- Invoke button shows loading state when dynamic prompts are processing, tooltip says generating
- Dynamic prompts preview icon in prompt box shows loading state when processing, tooltip says generating
There are a few breaking changes, which I've addressed.
The vast majority of changes are related to new handling of `reselect`'s `createSelector` options.
For better or worse, we memoize just about all our selectors using lodash `isEqual` for `resultEqualityCheck`. The upgrade requires we explicitly set the `memoize` option to `lruMemoize` to continue using lodash here.
Doing that required changing our `defaultSelectorOptions`.
Instead of changing that and finding dozens of instances where we weren't using that and instead were defining selector options manually, I've created a pre-configured selector: `createMemoizedSelector`.
This is now used everywhere instead of `createSelector`.
* first string only to test
* more strings changed
* almost half strings added in json file
* more strings added
* more changes
* few strings and t function changed
* resolved
* errors resolved
* chore(ui): fmt en.json
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
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.
- Change translations to use arrays of paragraphs instead of a single paragraph.
- Change component to accept a `feature` prop to identify the feature which the popover describes.
- Add optional `wrapperProps`: passed to the wrapper element, allowing more flexibility when using the popover
- Add optional `popoverProps`: passed to the `<Popover />` component, allowing for overriding individual instances of the popover's props
- Move definitions of features and popover settings to `invokeai/frontend/web/src/common/components/IAIInformationalPopover/constants.ts`
- Add some type safety to the `feature` prop
- Edit `POPOVER_DATA` to provide `image`, `href`, `buttonLabel`, and any popover props. The popover props are applied to all instances of the popover for the given feature. Note that the component prop `popoverProps` will override settings here.
- Remove the popover's arrow. Because the popover is wrapping groups of components, sometimes the error ends up pointing to nothing, which looks kinda janky. I've just removed the arrow entirely, but feel free to add it back if you think it looks better.
- Use a `link` variant button with external link icon to better communicate that clicking the button will open a new tab.
- Default the link button label to "Learn More" (if a label is provided, that will be used instead)
- Make default position `top`, but set manually set some to `right` - namely, anything with a dropdown. This prevents the popovers from obscuring or being obscured by the dropdowns.
- Do a bit more restructuring of the Popover component itself, and how it is integrated with other components
- More ref forwarding
- Make the open delay 1s
- Set the popovers to use lazy mounting (eg do not mount until the user opens the thing)
- Update the verbiage for many popover items and add missing dynamic prompts stuff
* 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>
There was a props on IAISlider to make the input component readonly - I didn't know this existed and at some point used a component with that prop as a template for other sliders, copying the flag over.
It's not actually used anywhere, so I removed the prop entirely, enabling the number inputs everywhere.
- Accordions now may be opened or closed regardless of whether or not their contents are enabled or active
- Accordions have a short text indicator alerting the user if their contents are enabled, either a simple `Enabled` or, for accordions like LoRA or ControlNet, `X Active` if any are active
