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.
* Added crop option to ImagePasteInvocation
ImagePasteInvocation extended the image with transparency when pasting outside of the base image's bounds. This introduces a new option to crop the resulting image back to the original base image.
* Updated version for ImagePasteInvocation as 3.1.1 was released.
Allow denying and explicitly allowing nodes. When a not-allowed node is used, a pydantic `ValidationError` will be raised.
- When collecting all invocations, check against the allowlist and denylist first. When pydantic constructs any unions related to nodes, the denied nodes will be omitted
- Add `allow_nodes` and `deny_nodes` to `InvokeAIAppConfig`. These are `Union[list[str], None]`, and may be populated with the `type` of invocations.
- When `allow_nodes` is `None`, allow all nodes, else if it is `list[str]`, only allow nodes in the list
- When `deny_nodes` is `None`, deny no nodes, else if it is `list[str]`, deny nodes in the list
- `deny_nodes` overrides `allow_nodes`
* Consolidated saturation/luminosity adjust.
Now allows increasing and inverting.
Accepts any color PIL format and channel designation.
* Updated docs/nodes/defaultNodes.md
* shortened tags list to channel types only
* fix typo in mode list
* split features into offset and multiply nodes
* Updated documentation
* Change invert to discrete boolean.
Previous math was unclear and had issues with 0 values.
* chore: black
* chore(ui): typegen
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
The `@invocation` decorator is extended with an optional `version` arg. On execution of the decorator, the version string is parsed using the `semver` package (this was an indirect dependency and has been added to `pyproject.toml`).
All built-in nodes are set with `version="1.0.0"`.
The version is added to the OpenAPI Schema for consumption by the client.
Initial support for polymorphic field types. Polymorphic types are a single of or list of a specific type. For example, `Union[str, list[str]]`.
Polymorphics do not yet have support for direct input in the UI (will come in the future). They will be forcibly set as Connection-only fields, in which case users will not be able to provide direct input to the field.
If a polymorphic should present as a singleton type - which would allow direct input - the node must provide an explicit type hint.
For example, `DenoiseLatents`' `CFG Scale` is polymorphic, but in the node editor, we want to present this as a number input. In the node definition, the field is given `ui_type=UIType.Float`, which tells the UI to treat this as a `float` field.
The connection validation logic will prevent connecting a collection to `CFG Scale` in this situation, because it is typed as `float`. The workaround is to disable validation from the settings to make this specific connection. A future improvement will resolve this.
This also introduces better support for collection field types. Like polymorphics, collection types are parsed automatically by the client and do not need any specific type hints.
Also like polymorphics, there is no support yet for direct input of collection types in the UI.
- Disabling validation in workflow editor now displays the visual hints for valid connections, but lets you connect to anything.
- Added `ui_order: int` to `InputField` and `OutputField`. The UI will use this, if present, to order fields in a node UI. See usage in `DenoiseLatents` for an example.
- Updated the field colors - duplicate colors have just been lightened a bit. It's not perfect but it was a quick fix.
- Field handles for collections are the same color as their single counterparts, but have a dark dot in the center of them.
- Field handles for polymorphics are a rounded square with dot in the middle.
- Removed all fields that just render `null` from `InputFieldRenderer`, replaced with a single fallback
- Removed logic in `zValidatedWorkflow`, which checked for existence of node templates for each node in a workflow. This logic introduced a circular dependency, due to importing the global redux `store` in order to get the node templates within a zod schema. It's actually fine to just leave this out entirely; The case of a missing node template is handled by the UI. Fixing it otherwise would introduce a substantial headache.
- Fixed the `ControlNetInvocation.control_model` field default, which was a string when it shouldn't have one.
All invocation metadata (type, title, tags and category) are now defined in decorators.
The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.
Category is a new invocation metadata, but it is not used by the frontend just yet.
- `@invocation()` decorator for invocations
```py
@invocation(
"sdxl_compel_prompt",
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
...
```
- `@invocation_output()` decorator for invocation outputs
```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
...
```
- update invocation docs
- add category to decorator
- regen frontend types
Doing this via `BaseInvocation`'s `Config.schema_extra()` means all clients get an accurate OpenAPI schema.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
Doing this via these classes' `Config.schema_extra()` method makes it unintrusive and clients will get the correct types for these properties.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
Refine concept of "parameter" nodes to "primitives":
- integer
- float
- string
- boolean
- image
- latents
- conditioning
- color
Each primitive has:
- A field definition, if it is not already python primitive value. The field is how this primitive value is passed between nodes. Collections are lists of the field in node definitions. ex: `ImageField` & `list[ImageField]`
- A single output class. ex: `ImageOutput`
- A collection output class. ex: `ImageCollectionOutput`
- A node, which functions to load or pass on the primitive value. ex: `ImageInvocation` (in this case, `ImageInvocation` replaces `LoadImage`)
Plus a number of related changes:
- Reorganize these into `primitives.py`
- Update all nodes and logic to use primitives
- Consolidate "prompt" outputs into "string" & "mask" into "image" (there's no reason for these to be different, the function identically)
- Update default graphs & tests
- Regen frontend types & minor frontend tidy related to changes
* Fix hue adjustment
Hue adjustment wasn't working correctly because color channels got swapped. This has now been fixed and we're using PIL rather than cv2 to do the RGBA->HSV->RGBA conversion. The range of hue adjustment is also the more typical 0..360 degrees.
orphaned since #3550 removed the LazilyLoadedModelGroup code, probably unused since ModelCache took over responsibility for sequential offload somewhere around #3335.
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
We can derive `isRefinerAvailable` from the query result (eg are there any refiner models installed). This is a piece of server state, so by using the list models response directly, we can avoid needing to manually keep the client in sync with the server.
Created a `useIsRefinerAvailable()` hook to return this boolean wherever it is needed.
Also updated the main models & refiner models endpoints to only return the appropriate models. Now we don't need to filter the data on these endpoints.
- add `RealESRGAN_x2plus.pth` model to installer
- add `RealESRGAN_x2plus.pth` to `realesrgan` node
- rename `RealESRGAN` to `ESRGAN` in nodes
- make `scale_factor` optional in `img_scale` node
- filename -> file_path
- pre and post prompt changed to optional
- clearer pre and post prompt descriptions
- handle pre and post prompt passed as None
- max_prompts defaults to 1 isted of 0 to avoid accidentally processing large prompt files with it set to 0 when adding a new node.
- remove dependency on having access to a `node` during emits, would need a bit of additional args passed through the system and I don't think its necessary at this point. this also allowed us to drop an extraneous fetching/parsing of the session from db.
- provide the invocation context to all `get_model()` calls, so the events are able to be emitted
- test all model loading events in the app and confirm socket events are received
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
This PR is to allow FP16 precision to work on Macs with MPS. In
addition, it centralizes the torch fixes/workarounds required for MPS
into a new backend utility `mps_fixes.py`. This is conditionally
imported in `api_app.py`/`cli_app.py`.
Many MANY thanks to @StAlKeR7779 for patiently working to debug and fix
these issues.
This PR is to allow FP16 precision to work on Macs with MPS. In addition, it centralizes the torch fixes/workarounds
required for MPS into a new backend utility file `mps_fixes.py`. This is conditionally imported in `api_app.py`/`cli_app.py`.
Many MANY thanks to StAlKeR7779 for patiently working to debug and fix these issues.
Only "real" conflicts were in:
invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
- 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.
* Testing change to LatentsToText to allow setting different cfg_scale values per diffusion step.
* Adding first attempt at float param easing node, using Penner easing functions.
* Core implementation of ControlNet and MultiControlNet.
* Added support for ControlNet and MultiControlNet to legacy non-nodal Txt2Img in backend/generator. Although backend/generator will likely disappear by v3.x, right now they are very useful for testing core ControlNet and MultiControlNet functionality while node codebase is rapidly evolving.
* Added example of using ControlNet with legacy Txt2Img generator
* Resolving rebase conflict
* Added first controlnet preprocessor node for canny edge detection.
* Initial port of controlnet node support from generator-based TextToImageInvocation node to latent-based TextToLatentsInvocation node
* Switching to ControlField for output from controlnet nodes.
* Resolving conflicts in rebase to origin/main
* Refactored ControlNet nodes so they subclass from PreprocessedControlInvocation, and only need to override run_processor(image) (instead of reimplementing invoke())
* changes to base class for controlnet nodes
* Added HED, LineArt, and OpenPose ControlNet nodes
* Added an additional "raw_processed_image" output port to controlnets, mainly so could route ImageField to a ShowImage node
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* More rebase repair.
* Added support for using multiple control nets. Unfortunately this breaks direct usage of Control node output port ==> TextToLatent control input port -- passing through a Collect node is now required. Working on fixing this...
* Fixed use of ControlNet control_weight parameter
* Fixed lint-ish formatting error
* Core implementation of ControlNet and MultiControlNet.
* Added first controlnet preprocessor node for canny edge detection.
* Initial port of controlnet node support from generator-based TextToImageInvocation node to latent-based TextToLatentsInvocation node
* Switching to ControlField for output from controlnet nodes.
* Refactored controlnet node to output ControlField that bundles control info.
* changes to base class for controlnet nodes
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* Cleaning up TextToLatent arg testing
* Cleaning up mistakes after rebase.
* Removed last bits of dtype and and device hardwiring from controlnet section
* Refactored ControNet support to consolidate multiple parameters into data struct. Also redid how multiple controlnets are handled.
* Added support for specifying which step iteration to start using
each ControlNet, and which step to end using each controlnet (specified as fraction of total steps)
* Cleaning up prior to submitting ControlNet PR. Mostly turning off diagnostic printing. Also fixed error when there is no controlnet input.
* Added dependency on controlnet-aux v0.0.3
* Commented out ZoeDetector. Will re-instate once there's a controlnet-aux release that supports it.
* Switched CotrolNet node modelname input from free text to default list of popular ControlNet model names.
* Fix to work with current stable release of controlnet_aux (v0.0.3). Turned of pre-processor params that were added post v0.0.3. Also change defaults for shuffle.
* Refactored most of controlnet code into its own method to declutter TextToLatents.invoke(), and make upcoming integration with LatentsToLatents easier.
* Cleaning up after ControlNet refactor in TextToLatentsInvocation
* Extended node-based ControlNet support to LatentsToLatentsInvocation.
* chore(ui): regen api client
* fix(ui): add value to conditioning field
* fix(ui): add control field type
* fix(ui): fix node ui type hints
* fix(nodes): controlnet input accepts list or single controlnet
* Moved to controlnet_aux v0.0.4, reinstated Zoe controlnet preprocessor. Also in pyproject.toml had to specify downgrade of timm to 0.6.13 _after_ controlnet-aux installs timm >= 0.9.2, because timm >0.6.13 breaks Zoe preprocessor.
* Core implementation of ControlNet and MultiControlNet.
* Added first controlnet preprocessor node for canny edge detection.
* Switching to ControlField for output from controlnet nodes.
* Resolving conflicts in rebase to origin/main
* Refactored ControlNet nodes so they subclass from PreprocessedControlInvocation, and only need to override run_processor(image) (instead of reimplementing invoke())
* changes to base class for controlnet nodes
* Added HED, LineArt, and OpenPose ControlNet nodes
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* Added support for using multiple control nets. Unfortunately this breaks direct usage of Control node output port ==> TextToLatent control input port -- passing through a Collect node is now required. Working on fixing this...
* Fixed use of ControlNet control_weight parameter
* Core implementation of ControlNet and MultiControlNet.
* Added first controlnet preprocessor node for canny edge detection.
* Initial port of controlnet node support from generator-based TextToImageInvocation node to latent-based TextToLatentsInvocation node
* Switching to ControlField for output from controlnet nodes.
* Refactored controlnet node to output ControlField that bundles control info.
* changes to base class for controlnet nodes
* Added more preprocessor nodes for:
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
* Prep for splitting pre-processor and controlnet nodes
* Refactored controlnet nodes: split out controlnet stuff into separate node, stripped controlnet stuff form image processing/analysis nodes.
* Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
* Cleaning up TextToLatent arg testing
* Cleaning up mistakes after rebase.
* Removed last bits of dtype and and device hardwiring from controlnet section
* Refactored ControNet support to consolidate multiple parameters into data struct. Also redid how multiple controlnets are handled.
* Added support for specifying which step iteration to start using
each ControlNet, and which step to end using each controlnet (specified as fraction of total steps)
* Cleaning up prior to submitting ControlNet PR. Mostly turning off diagnostic printing. Also fixed error when there is no controlnet input.
* Commented out ZoeDetector. Will re-instate once there's a controlnet-aux release that supports it.
* Switched CotrolNet node modelname input from free text to default list of popular ControlNet model names.
* Fix to work with current stable release of controlnet_aux (v0.0.3). Turned of pre-processor params that were added post v0.0.3. Also change defaults for shuffle.
* Refactored most of controlnet code into its own method to declutter TextToLatents.invoke(), and make upcoming integration with LatentsToLatents easier.
* Cleaning up after ControlNet refactor in TextToLatentsInvocation
* Extended node-based ControlNet support to LatentsToLatentsInvocation.
* chore(ui): regen api client
* fix(ui): fix node ui type hints
* fix(nodes): controlnet input accepts list or single controlnet
* Added Mediapipe image processor for use as ControlNet preprocessor.
Also hacked in ability to specify HF subfolder when loading ControlNet models from string.
* Fixed bug where MediapipFaceProcessorInvocation was ignoring max_faces and min_confidence params.
* Added nodes for float params: ParamFloatInvocation and FloatCollectionOutput. Also added FloatOutput.
* Added mediapipe install requirement. Should be able to remove once controlnet_aux package adds mediapipe to its requirements.
* Added float to FIELD_TYPE_MAP ins constants.ts
* Progress toward improvement in fieldTemplateBuilder.ts getFieldType()
* Fixed controlnet preprocessors and controlnet handling in TextToLatents to work with revised Image services.
* Cleaning up from merge, re-adding cfg_scale to FIELD_TYPE_MAP
* Making sure cfg_scale of type list[float] can be used in image metadata, to support param easing for cfg_scale
* Fixed math for per-step param easing.
* Added option to show plot of param value at each step
* Just cleaning up after adding param easing plot option, removing vestigial code.
* Modified control_weight ControlNet param to be polistmorphic --
can now be either a single float weight applied for all steps, or a list of floats of size total_steps, that specifies weight for each step.
* Added more informative error message when _validat_edge() throws an error.
* Just improving parm easing bar chart title to include easing type.
* Added requirement for easing-functions package
* Taking out some diagnostic prints.
* Added option to use both easing function and mirror of easing function together.
* Fixed recently introduced problem (when pulled in main), triggered by num_steps in StepParamEasingInvocation not having a default value -- just added default.
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
- Also fixed up order in which logger is created in invokeai-web
so that handlers are installed after command-line options are
parsed (and not before!)
- The invokeai.db database file has now been moved into
`INVOKEAIROOT/databases`. Using plural here for possible
future with more than one database file.
- Removed a few dangling debug messages that appeared during
testing.
- Rebuilt frontend to test web.
- 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
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
MidasDepth
ZoeDepth
MLSD
NormalBae
Pidi
LineartAnime
ContentShuffle
Removed pil_output options, ControlNet preprocessors should always output as PIL. Removed diagnostics and other general cleanup.
