We need to parse the config before doing anything related to invocations to ensure that the invocations union picks up on denied nodes.
- Move that to the top of api_app and cli_app
- Wrap subsequent imports in `if True:`, as a hack to satisfy flake8 and not have to noqa every line or the whole file
- Add tests to ensure graph validation fails when using a denied node, and that the invocations union does not have denied nodes (this indirectly provides confidence that the generated OpenAPI schema will not include denied nodes)
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
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>
- Node versions are now added to node templates
- Node data (including in workflows) include the version of the node
- On loading a workflow, we check to see if the node and template versions match exactly. If not, a warning is logged to console.
- The node info icon (top-right corner of node, which you may click to open the notes editor) now shows the version and mentions any issues.
- Some workflow validation logic has been shifted around and is now executed in a redux listener.
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.
* add StableDiffusionXLInpaintPipeline to probe list
* add StableDiffusionXLInpaintPipeline to probe list
* Blackified (?)
---------
Authored-by: Lincoln Stein <lstein@gmail.com>
Mucked about with to get it merged by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Add a click handler for node wrapper component that exclusively selects that node, IF no other modifier keys are held.
Technically I believe this means we are doubling up on the selection logic, as reactflow handles this internally also. But this is by far the most reliable way to fix the UX.
Copied into InvokeAI since IP-Adapter repo is not a package. Is there a better way to do this for non-packaged Python code while still keeping InvokeAI install easy?
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
