The helper function `generate_face_box_mask()` had a bug that prevented larger faces from being detected in some situations. This is resolved, and its dependent nodes (all the FaceTools nodes) have a patch version bump.
* node-FaceTools
* Added more documentation for facetools
* invert FaceMask masking
- FaceMask had face protected and surroundings change by default (face white, else black)
- Change to how FaceOff/others work: the opposite where surroundings protected, face changes by default (face black, else white)
* reflect changed facemask behaviour in docs
* add FaceOff+FaceMask workflows
- Add FaceOff and FaceMask example workflows to docs/workflows
* add FaceMask+FaceOff workflows to exampleworkflows.md
- used invokeai URL paths mimicking other workflow URLs, hopefully they translate when/if merged
* inheriting, typehints, black/isort/flake8
- modified FaceMask and FaceOff output classes to inherit base image, height, width from ImageOutput
- Added type annotations to helper functions, required some reworking of code's stored data
* remove credit header
- Was in my personal/repo copy, don't think it's necessary if merged.
* Optionals & image declaration duplication
- Added Optional[] to optional outputs and types
- removed duplication of image = context.services.images.get_pil_images(self.image.image_name) declaration
- Still need to find a way to deal with mask_pil None typing errors
* face(facetools): fix typing issues, add validation, clean up structure
* feat(facetools): update field descriptions
* Update FaceOff_FaceScale2x.json
- update FaceOff workflow after Bounded Image field removed in place of inheriting Image out field from ImageOutput
* feat(facetools): pass through original image on facemask if invalid face ids requested
* feat(facetools): tidy variable names & fn calls
* feat(facetools): bundle inter font, draw ids with it
Inter is a SIL Open Font license. The license is included and is fully permissive. Inter is the same font the UI and commercial application already uses.
Only the "regular" version is bundled.
* chore(facetools): isort & fix mypy issues
* docs(facetools): update and format docs
---------
Co-authored-by: Millun Atluri <millun.atluri@gmail.com>
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* 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>
* Add 'Random Float' node <3
does what it says on the tin :)
* Add random float + random seeded float nodes
altered my random float node as requested by Millu, kept the seeded version as an alternate variant for those that would like to control the randomization seed :)
* Update math.py
* Update math.py
* feat(nodes): standardize fields to match other nodes
---------
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
- Remove the add-to-board node
- Create `BoardField` field type & add it to `save_image` node
- Add UI for `BoardField`
- Tighten up some loose types
- Make `save_image` node, in workflow editor, default to not intermediate
- Patch bump `save_image`
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>
* Update collections.py
RangeOfSizeInvocation was not taking step into account when generating the end point of the range
* - updated the node description to refelect this mod
- added a gt=0 constraint to ensure only a positive size of the range
- moved the + 1 to be on the size. To ensure the range is the requested size in cases where the step is negative
- formatted with Black
* Removed +1 from the range calculation
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* New classes to support the PromptsFromFileInvocation Class
- PromptPosNegOutput
- PromptSplitNegInvocation
- PromptJoinInvocation
- PromptReplaceInvocation
* - Added PromptsToFileInvocation,
- PromptSplitNegInvocation
- now counts the bracket depth so ensures it cout the numbr of open and close brackets match.
- checks for escaped [ ] so ignores them if escaped e.g \[
- PromptReplaceInvocation - now has a user regex. and no regex in made caseinsesitive
* Update prompt.py
created class PromptsToFileInvocationOutput and use it in PromptsToFileInvocation instead of BaseInvocationOutput
* Update prompt.py
* Added schema_extra title and tags for PromptReplaceInvocation, PromptJoinInvocation, PromptSplitNegInvocation and PromptsToFileInvocation
* Added PTFileds Collect and Expand
* update to nodes v1
* added ui_type to file_path for PromptToFile
* update params for the primitive types used, remove the ui_type filepath, promptsToFile now only accepts collections until a fix is available
* updated the parameters for the StringOutput primitive
* moved the prompt tools nodes out of the prompt.py into prompt_tools.py
* more rework for v1
* added github link
* updated to use "@invocation"
* updated tags
* Adde new nodes PromptStrength and PromptStrengthsCombine
* chore: black
* feat(nodes): add version to prompt nodes
* renamed nodes from prompt related to string related. Also moved them into a strings.py file. Also moved and renamed the PromptsFromFileInvocation from prompt.py to strings.py. The PTfileds still remain in the Prompt_tool.py for now.
* added , version="1.0.0" to the invocations
* removed the PTField related nodes and the prompt-tools.py file all new nodes now live in the
* formatted prompt.py and strings.py with Black and fixed silly mistake in the new StringSplitInvocation
* - Revert Prompt.py back to original
- Update strings.py to be only StringJoin, StringJoinThre, StringReplace, StringSplitNeg, StringSplit
* applied isort to imports
* fix(nodes): typos in `strings.py`
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
This maps values to labels for multiple-choice fields.
This allows "enum" fields (i.e. `Literal["val1", "val2", ...]` fields) to use code-friendly string values for choices, but present this to the UI as human-friendly labels.
