Some asserts were bubbling up in places where they shouldn't have, causing errors when a node has a field without a matching template, or vice-versa.
To resolve this without sacrificing the runtime safety provided by asserts, a `InvocationFieldCheck` component now wraps all field components. This component renders a fallback when a field doesn't exist, so the inner components can safely use the asserts.
At some point, I made a mistake and imported the wrong types to some files for the old v1 and v2 workflow schema migration data.
The relevant zod schemas and inferred types have been restored.
This change doesn't alter runtime behaviour. Only type annotations.
Replace the `isCollection` and `isCollectionOrScalar` flags with a single enum value `cardinality`. Valid values are `SINGLE`, `COLLECTION` and `SINGLE_OR_COLLECTION`.
Why:
- The two flags were mutually exclusive, but this wasn't enforce. You could create a field type that had both `isCollection` and `isCollectionOrScalar` set to true, whuch makes no sense.
- There was no explicit declaration for scalar/single types.
- Checking if a type had only a single flag was tedious.
Thanks to a change a couple months back in which the workflows schema was revised, field types are internal implementation details. Changes to them are non-breaking.
Depending on the user behaviour and network conditions, it's possible that we could try to load a workflow before the invocation templates are available.
Fix is simple:
- Use the RTKQ query hook for openAPI schema in App.tsx
- Disable the load workflow buttons until w have templates parsed
Remove our DIY'd reducers, consolidating all node and edge mutations to use `edgesChanged` and `nodesChanged`, which are called by reactflow. This makes the API for manipulating nodes and edges less tangly and error-prone.
We now keep track of the original field type, derived from the python type annotation in addition to the override type provided by `ui_type`.
This makes `ui_type` work more like it sound like it should work - change the UI input component only.
Connection validation is extend to also check the original types. If there is any match between two fields' "final" or original types, we consider the connection valid.This change is backwards-compatible; there is no workflow migration needed.
When clearing the processor config, we shouldn't re-process the image. This logic wasn't handled correctly, but coincidentally the bug didn't cause a user-facing issue.
Without a config, we had a runtime error when trying to build the node for the processor graph and the listener failed.
So while we didn't re-process the image, it was because there was an error, not because the logic was correct.
Fix this by bailing if there is no image or config.
If you change the control model and the new model has the same default processor, we would still re-process the image, even if there was no need to do so.
With this change, if the image and processor config are unchanged, we bail out.
The previous super-minimal implementation had a major issue - the saved workflow didn't take into account batched field values. When generating with multiple iterations or dynamic prompts, the same workflow with the first prompt, seed, etc was stored in each image.
As a result, when the batch results in multiple queue items, only one of the images has the correct workflow - the others are mismatched.
To work around this, we can store the _graph_ in the image metadata (alongside the workflow, if generated via workflow editor). When loading a workflow from an image, we can choose to load the workflow or the graph, preferring the workflow.
Internally, we need to update images router image-saving services. The changes are minimal.
To avoid pydantic errors deserializing the graph, when we extract it from the image, we will leave it as stringified JSON and let the frontend's more sophisticated and flexible parsing handle it. The worklow is also changed to just return stringified JSON, so the API is consistent.
This stateful class provides abstractions for building a graph. It exposes graph methods like adding and removing nodes and edges.
The methods are documented, tested, and strongly typed.
Currently translated at 98.5% (1192 of 1210 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1192 of 1210 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1192 of 1210 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1192 of 1210 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 98.3% (1189 of 1209 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.3% (1189 of 1209 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (1209 of 1209 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1209 of 1209 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1188 of 1188 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1185 of 1185 strings)
Co-authored-by: Васянатор <ilabulanov339@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 71.9% (839 of 1166 strings)
Co-authored-by: Alexander Eichhorn <pfannkuchensack@einfach-doof.de>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI
Currently translated at 97.3% (1154 of 1185 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1174 of 1174 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1173 of 1173 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1166 of 1166 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1165 of 1165 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1149 of 1149 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (1147 of 1147 strings)
Co-authored-by: Васянатор <ilabulanov339@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 96.0% (1138 of 1185 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.4% (1156 of 1174 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.3% (1155 of 1174 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.4% (1129 of 1147 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Make the Invoke button show a loading spinner while queueing.
The queue mutations need to be awaited else the `isLoading` state doesn't work as expected. I feel like I should understand why, but I don't...
This allows comboboxes for models to have more granular groupings. For example, Control Adapter models can be grouped by base model & model type.
Before:
- `SD-1`
- `SDXL`
After:
- `SD-1 / ControlNet`
- `SD-1 / T2I Adapter`
- `SDXL / ControlNet`
- `SDXL / T2I Adapter`
When a control adapter processor config is changed, if we were already processing an image, that batch is immediately canceled. This prevents the processed image from getting stuck in a weird state if you change or reset the processor at the right (err, wrong?) moment.
- Update internal state for control adapters to track processor batches, instead of just having a flag indicating if the image is processing. Add a slice migration to not break the user's existing app state.
- Update preprocessor listener with more sophisticated logic to handle canceling the batch and resetting the processed image when the config changes or is reset.
- Fixed error handling that erroneously showed "failed to queue graph" errors when an active listener instance is canceled, need to check the abort signal.
This is largely an internal change, and it should have been this way from the start - less tip-toeing around layer types. The user-facing change is when you click an IP Adapter layer, it is highlighted. That's it.
Turns out, it's more efficient to just use the bbox logic for empty mask calculations. We already track if if the bbox needs updating, so this calculation does minimal work.
The dedicated calculation wasn't able to use the bbox tracking so it ran far more often than the bbox calculation.
Removed the "fast" bbox calculation logic, bc the new logic means we are continually updating the bbox in the background - not only when the user switches to the move tool and/or selects a layer.
The bbox calculation logic is split out from the bbox rendering logic to support this.
Result - better perf overall, with the empty mask handling retained.
Mask vector data includes additive (brush, rect) shapes and subtractive (eraser) shapes. A different composite operation is used to draw a shape, depending on whether it is additive or subtractive.
This means that a mask may have vector objects, but once rendered, is _visually_ empty (fully transparent). The only way determine if a mask is visually empty is to render it and check every pixel.
When we generate and save layer metadata, these fully erased masks are still used. Generating with an empty mask is a no-op in the backend, so we want to avoid this and not pollute graphs/metadata.
Previously, we did that pixel-based when calculating the bbox, which we only did when using the move tool, and only for the selected layer.
This change introduces a simpler function to check if a mask is transparent, and if so, deletes all its objects to reset it. This allows us skip these no-op layers entirely.
This check is debounced to 300 ms, trailing edge only.
When layer metadata is stored, the layer IDs are included. When recalling the metadata, we need to assign fresh IDs, else we can end up with multiple layers with the same ID, which of course causes all sorts of issues.
- Viewer only exists on Generation tab
- Viewer defaults to open
- When clicking the Control Layers tab on the left panel, close the viewer (i.e. open the CL editor)
- Do not switch to editor when adding layers (this is handled by clicking the Control Layers tab)
- Do not open viewer when single-clicking images in gallery
- _Do_ open viewer when _double_-clicking images in gallery
- Do not change viewer state when switching between app tabs (this no longer makes sense; the viewer only exists on generation tab)
- Change the button to a drop down menu that states what you are currently doing, e.g. Viewing vs Editing
There are unresolved platform-specific issues with this component, and its utility is debatable.
Should be easy to just revert this commit to add it back in the future if desired.
