- Rework hotkey data to include the keys for each hotkey action.
- Add wrapper for `useHotkeys` that accepts a hotkey category and id. Automatically selects the key from the hotkey data.
- Add handling for macOS (cmd vs ctrl, option vs alt).
- Redo all hotkey descriptions, deleting nonexistant ones.
- Some `esc` hotkeys that just close whatever you are currently in are omitted due to their relative simplicity and intuitiveness.
This was caused by allowing the stage to be set to fractional coordinates. For example, the stage might be positioned at `x: 142.22255, y: 488.79`.
When positioned like this, the canvas will be slightly misaligned with its native pixel grid. The browser does its best, but this causes tiny scaling artifacts throughout the image. It's most noticeable where there is a sharp contrast.
This behaviour was introduced while troubleshooting an issue with degraded quality when saving canvas to gallery. Turned out the stage position was unrelated to that issue, but I didn't realize that the change would cause this other type of problem.
The fix is super simple - ensure we floor stage coords when setting the manually. Konva never sets the position to fractional coordinates itself. For example, while dragging the stage, Konva sets the stage coordiantes itself, and they are always integers.
## Summary
This PR adds support for FLUX LoRA models on both quantized and
non-quantized base models.
Supported formats:
- diffusers
- kohya
Full changelist:
- Consolidated LoRA handling code in `invokeai/backend/lora`
- Add support for FLUX kohya and FLUX diffusers LoRA model loading
- Add ability to either patch LoRAs or run as a sidecar model (the
latter enables LoRAs to be applied to a wide range of quantized models).
## QA Instructions
Note to reviewers: I tested everything in this checklist. Feel free to
re-verify any of this, but also test any LoRAs that you have. There are
many small LoRA format variations, and there's a risk of breaking one of
them with this change.
FLUX LoRA
- [x] Import / probe of kohya FLUX LoRA
(https://civitai.com/models/159333/pokemon-trainer-sprite-pixelart?modelVersionId=779247)
- [x] Import / probe of Diffusers FLUX LoRA
(https://civitai.com/models/200255/hands-xl-sd-15-flux1-dev?modelVersionId=781855)
- [x] kohya with non-quantized base model
- [x] kohya with quantized base model (should roughly match the
non-quantized case)
- [x] diffusers with non-quantized base model
- [x] diffusers with quantized base model (should roughly match the
non-quantized case)
- [x] Sidecar LoRA patching speed (<0.1secs after model is loaded)
- [x] Stacking multiple fused LoRA models (i.e. on top on non-quantized
model)
- [x] Stacking multiple sidecar LoRA models (i.e. on top of quantized
model)
Regression Tests
- [x] SD1.5 LoRA (check output, speed and memory)
- [x] SDXL LoRA (check output, speed and memory)
- [x] `USE_MODULAR_DENOISE=1` smoke test with LoRA
Test for output regression with the following LoRA formats:
- [x] LoRA
- [x] LoHA
- [x] LoKr
- [x] IA3
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- Canvas manages its own progress socket event listeners and progress event data.
- Remove cancellations listener jank.
- Dip into low-level redux subscription API to watch for queue status changes, clearing the last "global" progress event when the queue has nothing in progress. Could also do this in a useEffect I guess.
- Had to shuffle some things around to prevent circular imports, so there are a lot of tiny changes here.
- Remove queue front button. Hold shift while clicking `Invoke` button to queue front.
- Restore queue menu actions w/ the reclaimed space.
- Simplify queue interaction hooks.
The lineart model often outputs a lot of almost-black noise. SD1.5 ControlNets seem to be OK with this, but SDXL ControlNets are not - they need a cleaner map. 12 was experimentally determined to be a good threshold, eliminating all the noise while keeping the actual edges. Other approaches to thresholding may be better, for example stretching the contrast or removing noise.
I tried:
- Simple thresholding (as implemented here) - works fine.
- Adaptive thresholding - doesn't work, because the thresholding is done in the context of small blocks, while we want thresholding in the context of the whole image.
- Gamma adjustment - alters the white values too much. Hard to tuen.
- Contrast stretching, with and without pre-simple-thresholding - this allows us to treshold out the noise, then stretch everything above the threshold down to almost-zero. So you have a smoother gradient of lightness near zero. It works but it also stretches contrast near white down a bit, which is probably undesired.
In the end, simple thresholding works fine and is very simple.
The HTML Canvas context has an `imageSmoothingEnabled` property which defaults to `true`. This causes the browser canvas API to, well, apply image smoothing - everything gets antialiased when drawn.
This is, of course, problematic when our goal is to be pixel-perfect. When the same image is drawn multiple times, we get progressive image degradation.
In `CanvasEntityObjectRenderer.cloneObjectGroup()`, where we use Konva's `Node.cache()` method to create a canvas from the entity's objects. Here, we were not setting `imageSmoothingEnabled` to false. This method is used very often by the compositor and we end up feeding back antialiased versions of the image data back into the canvas or generation backend.
Disabling smoothing here appears to fix the issue. I've also disabled image smoothing everywhere else we interact with a canvas rendering context.
The checkerboard background was rendered as a separate DOM element that stretched to fill the canvas container.
While the canvas width and height are always integers, this background element could have non-integer dimensions, depending on panel sizes.As a result, it could be slightly larger than the canvas, introducing a fine border around the canvas.
This is purely a visual issue, but it's very noticeable when you use the bbox overlay. It also can be noticed with masks that extend beyond the edge of the visible canvas.
- Refactor the checkerboard background to be rendered by the canvas instead of as a DOM element, resolving the issue.
- Add a helper method to get the scaled rect of the stage, updating a few places where we need such a rect.
- Rename `CanvasStageModule.getScaledPixels` method to `unscale`, clarifying its purpose.
Track various canvas states:
- Filtering an entity
- Transforming an entity
- Rasterizing an entity
- Compositing
- Busy (derived from all of the above)
Also track individual entity states:
- Locked
- Disabled
- All of type are hidden
- Has objects
- Interactable (derived from all of the above)
These states then gate various actions. For example:
- Cannot invoke while the canvas is busy.
- Cannot transform, filter, duplicate, or delete when the canvas is busy.
Tool interaction restrictions are not yet implemented.
## Summary
This PR splits the lora.py monolith into separate files. The main
motivation for doing this in a standalone PR is to make the diffs more
interpretable in the [upcoming
changes](https://github.com/invoke-ai/InvokeAI/compare/main...ryan/flux-lora-sidecar)
to support LoRAs for FLUX.
This PR does not make any functional changes - it just moves files
around and changes import paths.
## QA Instructions
I smoke tested generation with LoRA, LoHA, LoKr, and IA3.
## Merge Plan
No special instructions. Merge on approval.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- Add backcompat for cnet model default settings
- Default filter selection based on model type
- Updated UI components to use new filter nodes
- Added handling for failed filter executions, preventing filter from getting stuck in case it failed for some reason
- New translations for all filters & fields
Use a generic to narrow the `type` field from `string` to a literal. Now you can do e.g. `adapter.type === 'control_layer_adapter'` and TS narrows the type.
Similar to the existing node, but without any resizing. The backend logic was consolidated and modified so that it the model loading can be managed by the model manager.
The ONNX Runtime `InferenceSession` class was added to the `AnyModel` union to satisfy the type checker.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo. Unfortunately, this includes a whole git repo for EfficientNet. I believe we could use the package `timm` instead of this, but it's beyond me.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo.
So far, this includes:
- Save Canvas to Gallery
- Save Bbox to Gallery
- Send Bbox to Regional IP Adapter
- Send Bbox to Global IP Adapter
- Send Bbox to Control Layer
- Send Bbox to Raster Layer
To prevent losing all ephemeral canvas stage when switching tabs, we will refrain from destroying the canvas manager instance when its tab unmounts, and use the existing canvas manager instance on mount, if there is one.
One small change required in `CanvasStageModule` - a `setContainer` method to update the konva stage DOM element.
- Add `reset` functionality
- Rename badly named `autoPreviewFilter` to `autoProcessFilter`
- Do not process filter when starting, unless `autoProcessFilter` is enabled
This includes some fixes for the composite number input component's local value handling, resolving an infinite recursion problem when an invalid value is set.
Snap can be any of off, 8px or 64px.
The snap is used when moving and transforming entities.
When transforming and locking aspect ratio, the snap is ignored entirely, because we'd change the aspect ratio if we forced the snap.
Otherwise, if we are not locking aspect ratio (e.g. the user is holding shift), we snap the transform anchors to the grid.
Realized we can use listener middleware to respond to _actions_, as opposed to using the redux store subscription to respond to _state changes_... This might simplify some things.
Using this pattern here.
Only hiccup - there's a TS issue preventing this from being added to the state api module. The `addListener` method has an overloaded type signature and TS cannot extract the overloaded arg type using `Parameters<T>`. As a result, if we try to wrap this, we end up with a broken TS signature for the wrapper method.
There's a race condition where we sometimes get progress events from canceled queue items, depending on the timing of the cancellation request and last event or two from the queue item.
I can't imagine how to resolve this except by tracking all cancellations and ignoring events for cancelled items, which is implemented in this change.
- Add selectors to get the default control adapter and ip adapter with model, preferring controlnet over t2i adapter for model
- Add hooks to add each entity type, using the defaults
- Add hooks to add prompts/ip adapters to a regional guidance layer
- Use the defaults in other places where we add control layers or ip adapters (e.g. dnd-triggered entity creation)
- Each entity gets its own `CanvasEntityFilterer`
- Add auto-preview feature to filter, debounced by 1000ms leading + trailing
- Fix flash when preview updates
When resetting the canvas or staging area, we don't want to cancel generations that are going to the gallery - only those going to the canvas.
Thus the method should not cancel by origin, but instead cancel by destination.
Update the queue method and route.
Use the min of each pixel's alpha value and lightness for the output alpha. This prevents artifacts when using the transparency effect, especially with non-black pixels with low alpha.
- Rely on redux + reselect more
- Remove all nanostores that simply "mirrored" redux state in favor of direct subscriptions to redux store
- Add abstractions for creating redux subs and running selectors
- Add `initialize` method to CanvasModuleBase, for post-instantiation tasks
- Reduce local caching of state in modules to a minimum
Big cleanup. Makes these classes easier to implement, lots of comments and docstrings to clarify how it all works.
- Add default implementations for `destroy`, `repr` and `getLoggingContext`
- Tidy individual module configs
- Update `CanvasManager.buildLogger` to accept a canvas module as the arg
- Add `CanvasManager.buildPath`
TBH not sure exactly why this broke. Fixed by rollback back the use of a render prop in favor of global state. Also revised the API of `useBoolean` and `buildUseBoolean`.
- Canvas generation mode is replace with a boolean `sendToCanvas` flag. When off, images generated on the canvas go to the gallery. When on, they get added to the staging area.
- When an image result is received, if its destination is the canvas, staging is automatically started.
- Updated queue list to show the destination column.
- Added `IconSwitch` component to represent binary choices, used for the new `sendToCanvas` flag and image viewer toggle.
- Remove the queue actions menu in `QueueControls`. Move the queue count badge to the cancel button.
- Redo layout of `QueueControls` to prevent duplicate queue count badges.
- Fix issue where gallery and options panels could show thru transparent regions of queue tab.
- Disable panel hotkeys when on mm/queue tabs.
The frontend needs to know where queue items came from (i.e. which tab), and where results are going to (i.e. send images to gallery or canvas). The `origin` column is not quite enough to represent this cleanly.
A `destination` column provides the frontend what it needs to handle incoming generations.
This hook forcibly updates _all_ portals with `data-hidden=true` when the modal opens - then reverts it when the modal closes. It's intended to help screen readers. Unfortunately, this absolutely tanks performance because we have many portals. React needs to do alot of layout calculations (not re-renders).
IMO this behaviour is a bug in chakra. The modals which generated the portals are hidden by default, so this data attr should really be set by default. Dunno why it isn't.
Previously this badge, floating over the queue menu button next to the invoke button, was rendered within the existing layout. When I initially positioned it, the app layout interfered - it would extend into an area reserved for a flex gap, which cut off the badge.
As a (bad) workaround, I had shifted the whole app down a few pixels to make room for it. What I should have done is what I've done in this commit - render the badge in a portal to take it out of the layout so we don't need that extra vertical padding.
Sleekified some styling a bit too.
The canvas size was dynamic based on the container div's size. When the div was hidden (e.g. when selecting another tab), the container's effective size is 0. This resulted in the preview image canvas being drawn at a scale of 0.
Fixed by using an absolute size for the canvas container.
- Add lock toggle
- Tweak lock and enabled styles
- Update entity list action bar w/ delete & delete all
- Move add layer menu to action bar
- Adjust opacity slider style
- Throttle pushing to history for actions of the same type, starting with 1000ms throttle.
- History has a limit of 64 items, same as workflow editor
- Add clear history button
- Fix an issue where entity transformers would reset the entity state when the entity is fully transparent, resetting the redo stack. This could happen when you undo to the starting state of a layer
I learned that the inline selector syntax recreates the selector function on every render:
```ts
const val = useAppSelector((s) => s.slice.val)
```
Not good! Better is to create a selector outside the function and use it. Doing that for all selectors now, most of the way through now. Feels snappier.
Things like `$lastCursorPos` are now created within the canvas drawing classes. Consumers in react access them via `useCanvasManager`.
For example:
```tsx
const canvasManager = useCanvasManager();
const lastCursorPos = useStore(canvasManager.stateApi.$lastCursorPos);
```
Previously, canvas actions specific to an entity type only needed the id of that entity type. This allowed you to pass in the id of an entity of the wrong type.
All actions for a specific entity now take a full entity identifier, and the entity identifier type can be narrowed.
`selectEntity` and `selectEntityOrThrow` now need a full entity identifier, and narrow their return values to a specific entity type _if_ the entity identifier is narrowed.
The types for canvas entities are updated with optional type parameters for this purpose.
All reducers, actions and components have been updated.
While we lose the benefit of the caches persisting across reloads, this is a much simpler way to handle things. If we need a persistent cache, we can explore it in the future.
- use `stable-hash` to generate stable, non-crypto hashes for cache entries, instead of using deep object comparisons
- use an object to store image name caches
Sequence of events causing the race condition:
- Enqueue batch
- Invalidate `SessionQueueStatus` tag
- Request updated queue status via HTTP - batch still processing at this point
- Batch completes
- Event emitted saying so
- Optimistically update the queue status cache, it is correct
- HTTP request makes it back and overwrites the optimistic update, indicating the batch is still in progress
FIxed by not invalidating the cache.
Download events and invocation status events (including progress images) are very frequent. There's no real need for these to pass through redux. Handling them outside redux is a significant performance win - far fewer store subscription calls, far fewer trips through middleware.
All event handling is moved outside middleware. Cleanup of unused actions and listeners to follow.
- create a context for entity identifiers, massively simplifying UI for each entity int he list
- consolidate common redux actions
- remove now-unused code
The origin is an optional field indicating the queue item's origin. For example, "canvas" when the queue item originated from the canvas or "workflows" when the queue item originated from the workflows tab. If omitted, we assume the queue item originated from the API directly.
- Add migration to add the nullable column to the `session_queue` table.
- Update relevant event payloads with the new field.
- Add `cancel_by_origin` method to `session_queue` service and corresponding route. This is required for the canvas to bail out early when staging images.
- Add `origin` to both `SessionQueueItem` and `Batch` - it needs to be provided initially via the batch and then passed onto the queue item.
-
Instead of chaining konva `find` and `findOne` methods, all konva nodes are added to a mapping object. Finding and manipulating them is much simpler.
Done for regions and layers, wip for control adapters.
Subscribe to redux store directly, skipping all the react overhead.
With react in dev mode, a typical frame while using the brush tool on almost-empty canvas is reduced from ~7.5ms to ~3.5ms. All things considered, this still feels slow, but it's a massive improvement.
- Create separate object types for brush and eraser lines, instead of a single type that has a `tool` field.
- Create new object type for rect shapes.
- Add logic to schemas to migrate old object types to new.
- Update renderers & reducers.
Currently translated at 98.2% (1350 of 1374 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.2% (1350 of 1374 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.2% (1350 of 1374 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.4% (1349 of 1370 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.4% (1348 of 1369 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
* [MM] add API routes for getting & setting MM cache sizes, and retrieving MM stats
* Update invokeai/app/api/routers/model_manager.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* code cleanup after @ryand review
* Update invokeai/app/api/routers/model_manager.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* fix merge conflicts; tested and working
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
## Summary
This PR adds support for Image-to-Image and inpainting workflows with
the FLUX model.
Full changelog:
- Split out `FLUX VAE Encode` and `FLUX VAE Decode` nodes
- Renamed `FLUX Text-to-Image` node to `FLUX Denoise` (since it now
supports image-to-image too). This is a workflow-breaking change.
- Added support for FLUX image-to-image via the `Latents` param on the
FLUX denoising node.
- Added support for FLUX masked inpainting via the `Denoise Mask` param
on the FLUX denoising node.
- Added "Denoise Start" and "Denoise End" params to the "FLUX Denoise"
node.
- Updated the "FLUX Text to Image" default workflow.
- Added a "FLUX Image to Image" default workflow.
### Example
FLUX inpainting workflow
<img width="1282" alt="image"
src="https://github.com/user-attachments/assets/86fc1170-e620-4412-8fd8-e119f875fc2e">
Input image
Mask
Output image
### Callouts for reviewers:
- I renamed FLUXTextToImageInvocation -> FLUXDenoisingInvocation. This
is, of course, a breaking change. It feels like the right move and now
is the right time to do it. Any objection?
- I added new `FLUX VAE Encode` and `FLUX VAE Decode` nodes.
Alternatively, I could have tried to match these names to the
corresponding SD nodes (e.g. `FLUX Image to Latents`, `FLUX Latents to
Image`). Personally, I prefer the current names, but want to hear other
opinions.
### Usage notes:
- With the default dev timestep scheduler, the image structure is
largely determined in the first ~3 steps. A consequence of this is that
the denoise_start parameter provides limited 'granularity' of control.
This will likely be improved in the future as we add more scheduler
options. In the meantime, you will likely want to use small values for
`denoise_start` (e.g. 0.03) to start denoising on step ~1-4 out of ~30.
- Currently, there is no 'noise' parameter on the `FLUX Denoise` node,
so the `denoise_end` parameter has limited utility. This will be added
in the future.
## QA Instructions
Test the following workflows:
- [x] Vanilla FLUX text-to-image behaviour is unchanged
- [x] Image-to-image with FLUX dev, no mask
- [x] Image-to-image with FLUX dev, with mask
- [x] Image-to-image with FLUX schnell, no mask (smoke test, not
expected to work well)
## Merge Plan
No special instructions.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
Allocates the specified amount of VRAM, or allocates enough VRAM such that you have the specified amount of VRAM free.
Useful to simulate an environment with a specific amount of VRAM.
## Summary
This PR contains several improvements to memory management for FLUX
workflows.
It is now possible to achieve better FLUX model caching performance, but
this still requires users to manually configure their `ram`/`vram`
settings. E.g. a `vram` setting of 16.0 should allow for all quantized
FLUX models to be kept in memory on the GPU.
Changes:
- Check the size of a model on disk and free the requisite space in the
model cache before loading it. (This behaviour existed previously, but
was removed in https://github.com/invoke-ai/InvokeAI/pull/6072/files.
The removal did not seem to be intentional).
- Removed the hack to free 24GB of space in the cache before loading the
FLUX model.
- Split the T5 embedding and CLIP embedding steps into separate
functions so that the two models don't both have to be held in RAM at
the same time.
- Fix a bug in `InvokeLinear8bitLt` that was causing some tensors to be
left on the GPU when the model was offloaded to the CPU. (This class is
getting very messy due to the non-standard state_dict handling in
`bnb.nn.Linear8bitLt`. )
- Tidy up some dtype handling in FluxTextToImageInvocation to avoid
situations where we hold references to two copies of the same tensor
unnecessarily.
- (minor) Misc cleanup of ModelCache: improve docs and remove unused
vars.
Future:
We should revisit our default ram/vram configs. The current defaults are
very conservative, and users could see major performance improvements
from tuning these values.
## QA Instructions
I tested the FLUX workflow with the following configurations and
verified that the cache hit rates and memory usage matched the expected
behaviour:
- `ram = 16` and `vram = 16`
- `ram = 16` and `vram = 1`
- `ram = 1` and `vram = 1`
Note that the changes in this PR are not isolated to FLUX. Since we now
check the size of models on disk, we may see slight changes in model
cache offload patterns for other models as well.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
## Summary
- Add selectedStylePreset to app parameters
<!--A description of the changes in this PR. Include the kind of change
(fix, feature, docs, etc), the "why" and the "how". Screenshots or
videos are useful for frontend changes.-->
## Related Issues / Discussions
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
<!--WHEN APPLICABLE: Describe how you have tested the changes in this
PR. Provide enough detail that a reviewer can reproduce your tests.-->
## Merge Plan
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->
## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
These two scripts are broken and can cause data loss. Remove them.
They are not in the launcher script, but _are_ available to users in the terminal/file browser.
Hopefully, when we removing them here, `pip` will delete them on next installation of the package...
The root cause was the active style preset not being reset when it was deleted, or no longer present in the list of style presets.
- Add extra reducer to `stylePresetSlice` to reset the active preset if it is deleted or otherwise unavailable
- Update the dynamic prompts listener to trigger on delete/update/list of style presets
When invoke.sh is executed using a symlink with a working directory outside of InvokeAI's root directory, it will fail.
invoke.sh attempts to cd into the correct directory at the start of the script, but will cd into the directory of the symlink instead. This commit fixes that.
## Summary
Adds option to download all prompt templates to a CSV
## Related Issues / Discussions
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
<!--WHEN APPLICABLE: Describe how you have tested the changes in this
PR. Provide enough detail that a reviewer can reproduce your tests.-->
## Merge Plan
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->
## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
added a base prop for selectedWorkflow to allow loading a workflow on
launch
<!--A description of the changes in this PR. Include the kind of change
(fix, feature, docs, etc), the "why" and the "how". Screenshots or
videos are useful for frontend changes.-->
## Related Issues / Discussions
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
can test by loading InvokeAIUI with a selectedWorkflow prop of the
workflow ID
<!--WHEN APPLICABLE: Describe how you have tested the changes in this
PR. Provide enough detail that a reviewer can reproduce your tests.-->
## Merge Plan
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->
## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
- Enforce name is present and not an empty string
- Provide empty string as default for positive and negative prompt
- Add `positive_prompt` as validation alias for `prompt` field
- Strip whitespace automatically
- Create `TypeAdapter` to validate the whole list in one go
Currently translated at 98.5% (1336 of 1355 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1302 of 1321 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.6% (1302 of 1320 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
## Summary
Adds prompt templates to the UI. Demo video is attached.
* added default prompt templates to seed database on startup (these
cannot be edited or deleted by users via the UI)
* can create fresh prompt template, create from an image in gallery that
has prompt metadata, or copy an existing prompt template and modify
* if a template is active, can view what your prompt will be invoked as
by switching to "view mode"
https://github.com/user-attachments/assets/32d84e0c-b04c-48da-bae5-aa6eb685d209
## Related Issues / Discussions
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## QA Instructions
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## Merge Plan
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Around the time we (I) implemented pydantic events, I noticed a short pause between progress images every 4 or 5 steps when generating with SDXL. It didn't happen with SD1.5, but I did notice that with SD1.5, we'd get 4 or 5 progress events simultaneously. I'd expect one event every ~25ms, matching my it/s with SD1.5. Mysterious!
Digging in, I found an issue is related to our use of a synchronous queue for events. When the event queue is empty, we must call `asyncio.sleep` before checking again. We were sleeping for 100ms.
Said another way, every time we clear the event queue, we have to wait 100ms before another event can be dispatched, even if it is put on the queue immediately after we start waiting. In practice, this means our events get buffered into batches, dispatched once every 100ms.
This explains why I was getting batches of 4 or 5 SD1.5 progress events at once, but not the intermittent SDXL delay.
But this 100ms wait has another effect when the events are put on the queue in intervals that don't perfectly line up with the 100ms wait. This is most noticeable when the time between events is >100ms, and can add up to 100ms delay before the event is dispatched.
For example, say the queue is empty and we start a 100ms wait. Then, immediately after - like 0.01ms later - we push an event on to the queue. We still need to wait another 99.9ms before that event will be dispatched. That's the SDXL delay.
The easy fix is to reduce the sleep to something like 0.01 seconds, but this feels kinda dirty. Can't we just wait on the queue and dispatch every event immediately? Not with the normal synchronous queue - but we can with `asyncio.Queue`.
I switched the events queue to use `asyncio.Queue` (as seen in this commit), which lets us asynchronous wait on the queue in a loop.
Unfortunately, I ran into another issue - events now felt like their timing was inconsistent, but in a different way than with the 100ms sleep. The time between pushing events on the queue and dispatching them was not consistently ~0ms as I'd expect - it was highly variable from ~0ms up to ~100ms.
This is resolved by passing the asyncio loop directly into the events service and using its methods to create the task and interact with the queue. I don't fully understand why this resolved the issue, because either way we are interacting with the same event loop (as shown by `asyncio.get_running_loop()`). I suppose there's some scheduling magic happening.
There's a FastAPI bug that results in the OpenAPI spec outputting the same operation id for each operation when specifying multiple HTTP methods.
- Discussion: https://github.com/tiangolo/fastapi/discussions/8449
- Pending PR to fix: https://github.com/tiangolo/fastapi/pull/10694
In our case, we have a `get_image_full` endpoint that handles GET and HEAD.
This results in an invalid OpenAPI schema. A workaround is to use two route decorators for the operation handler. This works as expected - HEAD requests get the header, and GET requests get the resource. And the OpenAPI schema is valid.
- Updated the previous DepthAnything manual implementation to use the
`transformers` implementation instead. So we can get upstream features.
- Plugged in the DepthAnything models to be handled by Invoke's Model
Manager.
- `small_v2` model will use DepthAnythingV2. This has been added as a
new model option and is now also the default in the Linear UI.
# Merge
Review and merge.
Currently translated at 98.6% (1303 of 1321 strings)
translationBot(ui): update translation (Italian)
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Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
There was a problem w/ this release on windows and the builds were pulled from pypi. When installing invoke on windows, pip attempts to build from source, but most (all?) systems won't have the prerequisites for this and installs fail.
This also affects GH actions.
The simple fix is to exclude version 3.9.1 from our deps.
For more information, see https://github.com/matplotlib/matplotlib/issues/28551
## Summary
This PR enables Grounded SAM workflows
(https://arxiv.org/pdf/2401.14159) via the following:
- `GroundingDinoInvocation` for running a Grounding DINO model.
- `SegmentAnythingModelInvocation` for running a SAM model.
- `MaskTensorToImageInvocation` for convenient visualization.
Other notes:
- Uses the transformers implementation of Grounding DINO and SAM.
- The new models are treated as 'utility models' meaning that they are
not visible in the Models tab, and are downloaded automatically the
first time that they are used.
<img width="874" alt="image"
src="https://github.com/user-attachments/assets/1cbaa97d-0e27-4943-86b1-dc7327ba8675">
## Example
Input image
Prompt: "wheels", all other configs default
Result:
## Related Issues / Discussions
Thanks to @blessedcoolant for the initial draft here:
https://github.com/invoke-ai/InvokeAI/pull/6678
## QA Instructions
Manual tests:
- [ ] Test that default settings work well.
- [ ] Test with / without apply_polygon_refinement
- [ ] Test mask_filter options
- [ ] Test detection_threshold values
- [ ] Test RGB input image
- [ ] Test RGBA input image
- [ ] Test grayscale input image
- [ ] Smoke test that an empty mask is returned when 0 objects are
detected
- [ ] Test on CPU
- [ ] Test on MPS (Works on Mac OS, but had to force both models to run
on CPU instead of MPS)
Performance:
- Peak GPU memory utilization with both Grounding DINO and SAM models
loaded is ~4.5GB. (The models do not need to be loaded at the same time,
so could be offloaded by the MM if needed.)
- On an RTX4090, with the models already cached, node execution takes
~0.6 secs.
- On my CPU, with the models cached, node execution takes ~10secs.
## Merge Plan
No special instructions.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
## Summary
- we want a way to load the studio while being directed to a specific
tab, introduced a destination prop to achieve that
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## Summary
Code for lora patching from #6577.
Additionally made it the way, that lora can patch not only `weight`, but
also `bias`, because saw some loras which doing it.
## Related Issues / Discussions
#6606https://invokeai.notion.site/Modular-Stable-Diffusion-Backend-Design-Document-e8952daab5d5472faecdc4a72d377b0d
## QA Instructions
Run with and without set `USE_MODULAR_DENOISE` environment.
## Merge Plan
Replace old lora patcher with new after review done.
If you think that there should be some kind of tests - feel free to add.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
Gradient mask node outputs mask tensor with values in range [-1, 1],
which unexpected range for mask.
It handled in denoise node the way it translates to [0, 2] mask, which
looks even more wrongly)
From discussion with @dunkeroni I understand him as he thought that
negative values will be treated same as 0, so clamping values not change
intended node logic.
## Related Issues / Discussions
#6643
## QA Instructions
\-
## Merge Plan
\-
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
Add karras variants of `deis`, `unipc`, `kdpm2` and `kdpm_2_a`
schedulers.
Also added `dpmpp_3` schedulers, but `dpmpp_3s` currently bugged, so
added only 3m:
https://github.com/huggingface/diffusers/issues/9007
## Related Issues / Discussions
\-
## QA Instructions
\-
## Merge Plan
~@psychedelicious We need to decide what to do with schedulers order, as
it looks a bit broken:~
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
<!--A description of the changes in this PR. Include the kind of change
(fix, feature, docs, etc), the "why" and the "how". Screenshots or
videos are useful for frontend changes.-->
## Related Issues / Discussions
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discord. If this PR closes an issue, please use the "Closes #1234"
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## Merge Plan
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## Summary
Code for inpainting and inpaint models handling from
https://github.com/invoke-ai/InvokeAI/pull/6577.
Separated in 2 extensions as discussed briefly before, so wait for
discussion about such implementation.
## Related Issues / Discussions
#6606https://invokeai.notion.site/Modular-Stable-Diffusion-Backend-Design-Document-e8952daab5d5472faecdc4a72d377b0d
## QA Instructions
Run with and without set `USE_MODULAR_DENOISE` environment.
Try and compare outputs between backends in cases:
- Normal generation on inpaint model
- Inpainting on inpaint model
- Inpainting on normal model
## Merge Plan
Nope.
If you think that there should be some kind of tests - feel free to add.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
We were checking the selected and auto-add board ids against the query cache to see if they still exist. If not, we reset.
This only works if the query cache is updated by the time we do the check - race condition!
We already have the board id from the query args, so there's no need to check the query cache - just compare the deleted board ID directly.
Previously this file's several listeners were all in a single one and I had adapted/split its logic up a bit wonkily, introducing these problems.
The logic was incorrect in two ways:
1. We only ran the logic if we _enable_ showing archived boards. It should be run we we _disable_ showing archived boards.
2. If we couldn't find the selected board in the query cache, we didn't do the reset. This is wrong - if the board isn't in the query cache, we _should_ do the reset. This inverted logic makes more sense before the fix for issue 1.
## Summary
T2I Adapter code from #6577.
## Related Issues / Discussions
#6606https://invokeai.notion.site/Modular-Stable-Diffusion-Backend-Design-Document-e8952daab5d5472faecdc4a72d377b0d
## QA Instructions
Run with and without set `USE_MODULAR_DENOISE` environment.
## Merge Plan
Nope.
If you think that there should be some kind of tests - feel free to add.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
Seamless code from #6577.
## Related Issues / Discussions
#6606https://invokeai.notion.site/Modular-Stable-Diffusion-Backend-Design-Document-e8952daab5d5472faecdc4a72d377b0d
## QA Instructions
Run with and without set `USE_MODULAR_DENOISE` environment.
## Merge Plan
Nope.
If you think that there should be some kind of tests - feel free to add.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
The model edit UI's composition allows for the model edit form to be instantiated before the model's config has been received. This results in the form having no values - all the fields are blank instead of populated by the model config.
Part of the fix is to pass the model config around directly instead of relying on _all_ components to fetch the model directly.
I also fixed a crapload of performance issues related to improper use of redux selectors.
Problems this was causing:
- Deleting an edge was a copy of another edge deletes both edges
- Deleting a node that was a copy-with-edges of another node deletes its edges and it's original edges, leaving what I will call "ghost noodles" behind
Previously you could spam the next/prev buttons and really thrash the server. Throttled to 500ms, which feels like a happy medium between responsive and not-thrash-y.
- Autofocus on popover open
- Autoselect number on popover open
- Enter works to change page when input is focused
- Esc works to close popover when input is focused
It was possible to clear the search term while a debounced setSearchTerm is still pending. This resulted in the gallery getting out of sync w/ the search term.
To fix this, we need to lift the state up a bit and cancel any pending debounced setSearchTerm calls when closing the search or clearing the search term box.
`spandrel_image_to_image` now just runs the model with no changes.
`spandrel_image_to_image_autoscale` runs the model repeatedly until the desired scale is reached. previously, `spandrel_image_to_image` did this.
* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* documentation fix
* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* documentation fix
* remove v9 pnpm lockfile
* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* remove v9 pnpm lockfile
* regenerate schema.ts
* prettified
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
## Summary
Update Simple Upscale Button to work with spandrel models, add
UpscaleWarning when models aren't available, clean up ESRGAN logic
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## Related Issues / Discussions
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## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
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- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
ControlNet code from #6577.
## Related Issues / Discussions
#6606https://invokeai.notion.site/Modular-Stable-Diffusion-Backend-Design-Document-e8952daab5d5472faecdc4a72d377b0d
## QA Instructions
Run with and without set `USE_MODULAR_DENOISE` environment.
## Merge Plan
Merge #6641 firstly, to be able see output difference properly.
If you think that there should be some kind of tests - feel free to add.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
Rescale CFG code from #6577.
## Related Issues / Discussions
#6606https://invokeai.notion.site/Modular-Stable-Diffusion-Backend-Design-Document-e8952daab5d5472faecdc4a72d377b0d
## QA Instructions
Run with and without set `USE_MODULAR_DENOISE` environment.
~~Note: for some reasons there slightly different output from run to
run, but I able sometimes to get same output on main and this branch.~~
Fix presented in #6641.
## Merge Plan
~~Nope.~~ Merge #6641 firstly, to be able see output difference
properly.
If you think that there should be some kind of tests - feel free to add.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
- currently the total for uncategorized images is not updating when
moving and deleting images, this will update that count when making
those actions
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## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
Fix function call that we forgot to update in #6606
## QA Instructions
Run a TiledMultiDiffusionDenoiseLatents invocation and make sure it
doesn't crash.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
## Summary
Base code of new modular backend from #6577.
Contains normal generation and regional prompts support.
Also preview extension included to test if extensions logic works.
## Related Issues / Discussions
https://invokeai.notion.site/Modular-Stable-Diffusion-Backend-Design-Document-e8952daab5d5472faecdc4a72d377b0d
## QA Instructions
Run with and without set `USE_MODULAR_DENOISE` environment.
Currently only normal and regional conditionings supported, so just
generate some images and compare with main output.
## Merge Plan
Discuss a bit more about injection point names?
As if for example in future unet will be overridable, current
`pre_unet`/`post_unet` assumes to name override as `unet` what feels a
bit odd.
Also `apply_cfg` - future implementation could ignore/not use cfg, so in
this case `combine_noise_predictions`/`combine_noise` seems more
suitable.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
This PR adds some spandrel upscale models to the starter model list.
In the future we may also want to add:
- Some DAT models
(https://drive.google.com/drive/folders/1iBdf_-LVZuz_PAbFtuxSKd_11RL1YKxM)
## QA Instructions
I installed the starter models via the model manager UI, and tested that
I could use them in a workflow.
## Merge Plan
- [ ] Merge the preceding Spandrel PRs first, then change the target
branch to `main`.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
## Summary
Add tiling to the `SpandrelImageToImageInvocation` node so that it can
process large images.
Tiling enables this node to run on effectively any input image
dimension. Of course, the computation time increases quadratically with
the image dimension.
Some profiling results on an RTX4090:
- Input 1024x1024, 4x upscale, 4x UltraSharp ESRGAN: `13 secs`, `<4 GB
VRAM`
- Input 4096x4096, 4x upscale, 4x UltraSharop ESRGAN: `46 secs`, `<4 GB
VRAM`
- Input 4096x4096, 2x upscale, SwinIR: `165 secs`, `<5 GB VRAM`
A lot of the time is spent PNG encoding the final image:
- PNG encoding of a 16384x16384 image takes `83secs @
pil_compress_level=7`, `24secs @ pil_compress_level=1`
Callout: If we want to start building workflows that pass large images
between nodes, we are going to have to find a way to avoid the PNG
encode/decode roundtrip that we are currently doing. As is, we will be
incurring a huge penalty for every node that receives/produces a large
image.
## QA Instructions
- [x] Tested with tiling up to 4096x4096 -> 16384x16384.
- [x] Test on images with an alpha channel (the alpha channel is
dropped).
- [x] Test on images with odd dimension.
- [x] Test no tiling (`tile_size=0`)
## Merge Plan
- [x] Merge #6556 first, and change the target branch to `main`.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
## Summary
- Add support for all
[spandrel](https://github.com/chaiNNer-org/spandrel) image-to-image
models - this is a collection of many popular super-resolution models
(e.g. ESRGAN, Real-ESRGAN, SwinIR, DAT, etc.)
Examples of supported models:
- DAT:
https://drive.google.com/drive/folders/1iBdf_-LVZuz_PAbFtuxSKd_11RL1YKxM
- SwinIR: https://github.com/JingyunLiang/SwinIR/releases
- Any ESRGAN / Real-ESRGAN model
## Related Issues
Closes#6394
## QA Instructions
- [x] Test that unsupported models still fail the probe (i.e. no false
positive spandrel models)
- [x] Test adding a few non-spandrel model types
- [x] Test adding a handful of spandrel model types: ESRGAN,
Real-ESRGAN, SwinIR, DAT
- [x] Verify model size estimation for the model cache
- [x] Test using the spandrel models in a practical image upscaling
workflow
## Merge Plan
- [x] Get approval from @brandonrising and @maryhipp before merging -
this PR has commercial implications.
- [x] Merge #6571 and change the target branch to `main`
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
In #6490 we enabled non-blocking torch device transfers throughout the model manager's memory management code. When using this torch feature, torch attempts to wait until the tensor transfer has completed before allowing any access to the tensor. Theoretically, that should make this a safe feature to use.
This provides a small performance improvement but causes race conditions in some situations. Specific platforms/systems are affected, and complicated data dependencies can make this unsafe.
- Intermittent black images on MPS devices - reported on discord and #6545, fixed with special handling in #6549.
- Intermittent OOMs and black images on a P4000 GPU on Windows - reported in #6613, fixed in this commit.
On my system, I haven't experience any issues with generation, but targeted testing of non-blocking ops did expose a race condition when moving tensors from CUDA to CPU.
One workaround is to use torch streams with manual sync points. Our application logic is complicated enough that this would be a lot of work and feels ripe for edge cases and missed spots.
Much safer is to fully revert non-locking - which is what this change does.
This issue is caused by a race condition. When a large image is served to the client, it is done using a streaming `FileResponse`. This concurrently serves the image straight from disk. The file is kept open by FastAPI until the image is fully served.
When a user deletes an image before the file is done serving, the delete fails because the file is still held by FastAPI.
To reproduce the issue:
- Create a very large image (8k reliably creates the issue).
- Create a smaller image, so that the first image in the gallery is not the large image.
- Refresh the app. The small image should be selected.
- Select the large image and immediately delete it. You have to be fast, to delete it before it finishes loading.
- In the terminal, we expect to see an error saying `Failed to delete image file`, and the image does not disappear from the UI.
- After a short wait, once the image has fully loaded, try deleting it again. We expect this to work.
The workaround is to instead serve the image from memory.
Loading the image to memory is very fast, so there is only a tiny window in which we could create the race condition, but it technically could still occur, because FastAPI is asynchronous and handles requests concurrently.
Once we load the image into memory, deletions of that image will work. Then we return a normal `Response` object with the image bytes. This is essentially what `FileResponse` does - except it uses `anyio.open_file`, which is async.
The tradeoff is that the server thread is blocked while opening the file. I think this is a fair tradeoff.
A future enhancement could be to implement soft deletion of images (db is already set up for this), and then clean up deleted image files on startup/shutdown. We could move back to using the async `FileResponse` for best responsiveness in the server without any risk of race conditions.
For some reason, I started getting this indefinite hang when the app checks if port 9090 is available. After some fiddling around, I found that adding a timeout resolves the issue.
I confirmed that the util still works by starting the app on 9090, then starting a second instance. The second instance correctly saw 9090 in use and moved to 9091.
## Summary
This PR changes the handling of invalid model configs in the DB to log a
warning rather than crashing the app.
This change is being made in preparation for some upcoming new model
additions. Previously, if a user rolled back from an app version that
added a new model type, the app would not launch until the DB was fixed.
This PR changes this behaviour to allow rollbacks of this type (with
warnings).
**Keep in mind that this change is only helpful to users _rolling back
to a version that has this fix_. I.e. it offers no help in the first
version that includes it.**
## QA Instructions
1. Run the Spandrel model branch, which adds a new model type
https://github.com/invoke-ai/InvokeAI/pull/6556.
2. Add a spandrel model via the model manager.
3. Rollback to main. The app will crash on launch due to the invalid
spandrel model config.
4. Checkout this branch. The app should now run with warnings about the
invalid model config.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
Currently translated at 100.0% (1282 of 1282 strings)
translationBot(ui): update translation (Russian)
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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 98.2% (1260 of 1282 strings)
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Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
- Refine layout
- Update colors - more minimal, fewer shaded boxes
- Add indicator for search icons showing a search term is entered
- Handle new `projectName` and `projectUrl` ui props
## Summary
Update Boards UI in the gallery and adds support for creating and
displaying private boards
<!--A description of the changes in this PR. Include the kind of change
(fix, feature, docs, etc), the "why" and the "how". Screenshots or
videos are useful for frontend changes.-->
## Related Issues / Discussions
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
Can view private boards by setting config.allowPrivateBoards to true
<!--WHEN APPLICABLE: Describe how you have tested the changes in this
PR. Provide enough detail that a reviewer can reproduce your tests.-->
## Merge Plan
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->
## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
Demote error log to warning for models treated as having size 0.
## Related Issues / Discussions
Closes#6587
I looked into handling ESRGAN model sizes properly. They load a
state_dict with a bit of an unusual nested-dict structure. Rather than
figure out how to accurately calculate their size, we can just wait for
https://github.com/invoke-ai/InvokeAI/pull/6556. ESRGAN model size
handling should work properly when loaded through that pathway.
## QA Instructions
Loaded an ESRGAN model, and confirmed that the warning log is at the
warning level.
## Merge Plan
No special instructions.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
This commit corrects a broken link on line 16 that was pointing to the latest release but causing a 404 error (page not found) when clicked. The issue was identified as a trailing dot at the end of the URL, which has now been removed. This ensures users can access the intended latest release page.
## Summary
This PR tweaks the wording of the PR template QA instructions with the
goals of:
1. Make it more clear that PR authors are responsible for testing their
PRs.
2. Encouraging sufficient detail in the test descriptions.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
## Summary
Delete an unused duplicate libc_util.py file. The active version is at
`invokeai/backend/model_manager/libc_util.py`
## QA Instructions
I ran a smoke test to confirm that memory snapshotting still works.
## Merge Plan
- [x] Change target branch to `main` before merging.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
## Summary
This PR migrates all relative imports to absolute imports, and adds a
ruff check to enforce this going forward.
The justification for this change is here:
https://github.com/invoke-ai/InvokeAI/issues/6575
## QA Instructions
Smoke test all common workflows. Most of the relative -> absolute
conversions could be completed automatically, so the risk is relatively
low.
## Merge Plan
As with any far-reaching change like this, it is likely to cause some
merge conflicts with some in-flight branches. Unfortunately, there's no
way around this, but let me know if you can think of in-flight work that
will be significantly disrupted by this.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_ N/A
- [x] _Documentation added / updated (if applicable)_ N/A
## Summary
This PR fixes a regression that caused the following models to be
treated as having size 0 in the model cache: `(TextualInversionModelRaw,
IPAdapter, LoRAModelRaw)`.
Changes:
- Call the correct model size calculation for all supported model types.
- Log an error message if an unexpected model type is loaded, to prevent
similar regressions in the future.
## QA Instructions
I tested the following features and verified that no models fell back to
using a size of 0 unexpectedly:
- Test-to-image
- Textual Inversion
- LoRA
- IP-Adapter
- ControlNet
(All tested with both SD1.5 and SDXL.)
I compared the model cache switching behavior before and after this
change with a large number of LoRAs (10). Since LoRAs are small compared
to the main models, the changes in behaviour are minimal. Nonetheless,
it makes sense to get this in for correctness. And it might make a
difference for some usage patterns with limited RAM.
## Merge Plan
No special instructions.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- For single image deletion, select the image in the same slot as the deleted image
- For multiple image deletion, empty selection
- On list images, if no images are currently selected, select the first image
## Summary
- This PR exposes a `tile_size` field on `ImageToLatentsInvocation` and
`LatentsToImageInvocation`.
- Setting `tile_size = 0` preserves the default behaviour.
- This feature is primarily intended to support upscaling workflows that
require VAE encoding/decoding high resolution images. In the future, we
may want to expose the tile size as a global application config, but
that's a separate conversation.
- As a general rule, larger tile sizes produce better results at the
cost of higher memory usage.
### Example:
Original (5472x5472)
VAE roundtrip with 512x512 tiles (note the discoloration)
VAE roundtrip with 1024x1024 tiles (some discoloration still present,
but less severe than at 512x512)
## Related Issues / Discussions
Related: #6144
## QA Instructions
- [x] Test image generation via the Linear tab
- [x] Test VAE roundtrip with tiling disabled
- [x] Test VAE roundtrip with tiling and tile_size = 0
- [x] Test VAE roundtrip with tiling and tile_size > 0
## Merge Plan
No special instructions.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
The selection logic is a bit complicated. We have image selection and pagination, both of which can be triggered using the mouse or hotkeys. We have viewer image selection and comparison image selection, which is determined by the alt key.
This change ties the room together with these behaviours:
- Changing the page using pagination buttons never changes the selection.
- Changing the selected image using arrows may change the page, if the arrow key pressed would select an image off the current page.
- `right` on the last image of the current page goes to the next page
- `down` on the last row of images goes to the next page
- `left` on the first image of the current page goes to the previous page
- `up` on the first row of images goes to the previous page
- If `alt` is held when using arrow keys, we change the page, but we only change the comparison image selection.
- When using arrow keys, if the page has changed since the last image was selected, the selection is reset to the first image on the page.
- The next/previous buttons on the image viewer do the same thing as `left` and `right` without `alt`.
- When clicking an image in the gallery:
- If no modifier keys are held, the image is exclusively selected.
- If `ctrl` or `meta` are held, the image's selection status is toggled.
- If `shift` is held, all images from the last-selected image to the image are selected. If there are no images on the current page, the selection is unchanged.
- If `alt` is held, the image is set as the compare image.
- `ctrl+a` and `meta+a` add the current page to the selection.
The logic for gallery navigation and selection is now pretty hairy. It's spread across 3 hooks, a listener, redux slice, components.
When we next make changes to this part of the app, we should consider consolidating some of the related logic. Probably most of it can go into a single listener and make it much simpler to grok.
Don't like this UI (even though I suggested it). No need to prevent the user from interacting with the search term field during fetching. Let's figure out a nicer way to present this in a followup.
## Summary
Python 3.11 has a wonderfully devious breaking change where _sometimes_
using enum classes that inherit from `str` or `int` do not work the same
way as they do in 3.10 when used within string formatting/interpolation.
This breaks the new gallery sort queries. The fix is to use
`order_dir.value` instead of `order_dir` in the query.
This was not an issue during development because the feature was
developed w/ python 3.10.
## Related Issues / Discussions
Thanks to @JPPhoto for reporting and troubleshooting:
https://discord.com/channels/1020123559063990373/1149513625321603162/1256211815982039173
## QA Instructions
JP's fancy python 3.11 system should work on this PR.
## Merge Plan
n/a
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
If the currently selected or auto-add board is archived or deleted, we should reset them. There are some edge cases taht weren't handled in the previous implementation.
All handling of this logic is moved to the (renamed) listener.
Before this change, if you attempt to create an image that with a nonexistent board, we'd get an unhandled error when adding the image to a board. The record would be created, but file not, due to the structure of the code.
With this change, we now log a warning if we have a problem adding the image to the board, but the record and file are still created.
A future improvement would be to create a transaction for this part of the code, preventing some other situation that could result in only the record or only the file beings saved.
* use model_class.load_singlefile() instead of converting; works, but performance is poor
* adjust the convert api - not right just yet
* working, needs sql migrator update
* rename migration_11 before conflict merge with main
* Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* implement lightweight version-by-version config migration
* simplified config schema migration code
* associate sdxl config with sdxl VAEs
* remove use of original_config_file in load_single_file()
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
## Summary
We can get black outputs when moving tensors from CPU to MPS. It appears
MPS to CPU is fine. See:
- https://github.com/pytorch/pytorch/issues/107455
-
https://discuss.pytorch.org/t/should-we-set-non-blocking-to-true/38234/28
Changes:
- Add properties for each device on `TorchDevice` as a convenience.
- Add `get_non_blocking` static method on `TorchDevice`. This utility
takes a torch device and returns the flag to be used for non_blocking
when moving a tensor to the device provided.
- Update model patching and caching APIs to use this new utility.
## Related Issues / Discussions
Fixes: #6545
## QA Instructions
For both MPS and CUDA:
- Generate at least 5 images using LoRAs
- Generate at least 5 images using IP Adapters
## Merge Plan
We have pagination merged into `main` but aren't ready for that to be
released.
Once this fix is tested and merged, we will probably want to create a
`v4.2.5post1` branch off the `v4.2.5` tag, cherry-pick the fix and do a
release from the hotfix branch.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_ @RyanJDick @lstein This
feels testable but I'm not sure how.
- [ ] _Documentation added / updated (if applicable)_
We can get black outputs when moving tensors from CPU to MPS. It appears MPS to CPU is fine. See:
- https://github.com/pytorch/pytorch/issues/107455
- https://discuss.pytorch.org/t/should-we-set-non-blocking-to-true/38234/28
Changes:
- Add properties for each device on `TorchDevice` as a convenience.
- Add `get_non_blocking` static method on `TorchDevice`. This utility takes a torch device and returns the flag to be used for non_blocking when moving a tensor to the device provided.
- Update model patching and caching APIs to use this new utility.
Fixes: #6545
We only need to show the totals in the tooltip. Tooltips accpet a component for the tooltip label. The component isn't rendered until the tooltip is triggered.
Move the board total fetching into a tooltip component for the boards. Now we only fire these requests when the user mouses over the board
- Simplify the gallery layout
- Set an initial gallery limit to load _some_ images immediately.
- Refactor the resize observer to use the actual rendered image component to calculate the number of images per row/col. This prevents inaccuracies caused by image padding that could result in the wrong number of images.
- Debounce the limit update to not thrash teh API
- Use absolute positioning trick to ensure the gallery container is always exactly the right size
- Minimum of `imagesPerRow` images loaded at all times
This is one of those unexpected CSS quirks. Flex containers need min-width or min-height for their children to not overflow. Add `minH={0}` to gallery container.
## Summary
https://github.com/invoke-ai/InvokeAI/pull/6522 introduced a change in
behavior in cases where start/end were set such that there are 0
timesteps. This PR reverts that change.
cc @StAlKeR7779
## QA Instructions
Run with euler, 5 steps, start: 0.0, end: 0.05. I ran this test before
#6522, after #6522, and on this branch. This branch restores the
behavior to pre-#6522 i.e. noise is injected even if no denoising steps
are applied.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
* add support for probing and loading SDXL VAE checkpoint files
* broaden regexp probe for SDXL VAEs
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
## Summary
No functional changes, just cleaning some things up as I touch the code.
This PR cleans up the `SilenceWarnings` context manager:
- Fix type errors
- Enable SilenceWarnings to be used as both a context manager and a
decorator
- Remove duplicate implementation
- Check the initial verbosity on `__enter__()` rather than `__init__()`
- Save an indentation level in DenoiseLatents
## QA Instructions
I generated an image to confirm that warnings are still muted.
## Merge Plan
- [x] ⚠️ Merge https://github.com/invoke-ai/InvokeAI/pull/6492 first,
then change the target branch to `main`.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- Fix type errors
- Enable SilenceWarnings to be used as both a context manager and a decorator
- Remove duplicate implementation
- Check the initial verbosity on __enter__() rather than __init__()
## Summary
added route to install huggingface models from model marketplace
<!--A description of the changes in this PR. Include the kind of change
(fix, feature, docs, etc), the "why" and the "how". Screenshots or
videos are useful for frontend changes.-->
## Related Issues / Discussions
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
test by going to
http://localhost:5173/api/v2/models/install/huggingface?source=${hfRepo}
<!--WHEN APPLICABLE: Describe how we can test the changes in this PR.-->
## Merge Plan
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->
## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
When a model install is initiated from outside the client, we now trigger the model manager tab's model install list to update.
- Handle new `model_install_download_started` event
- Handle `model_install_download_complete` event (this event is not new but was never handled)
- Update optimistic updates/cache invalidation logic to efficiently update the model install list
Previously, we used `model_install_download_progress` for both download starting and progressing. When handling this event, we don't know which actual thing it represents.
Add `model_install_download_started` event to explicitly represent a model download started event.
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* do not save original weights if there is a CPU copy of state dict
* Update invokeai/backend/model_manager/load/load_base.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* documentation fixes requested during penultimate review
* add non-blocking=True parameters to several torch.nn.Module.to() calls, for slight performance increases
* fix ruff errors
* prevent crash on non-cuda-enabled systems
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
## Summary
This three two model manager-related methods to the InvocationContext
uniform API. They are accessible via `context.models.*`:
1. **`load_local_model(model_path: Path, loader:
Optional[Callable[[Path], AnyModel]] = None) ->
LoadedModelWithoutConfig`**
*Load the model located at the indicated path.*
This will load a local model (.safetensors, .ckpt or diffusers
directory) into the model manager RAM cache and return its
`LoadedModelWithoutConfig`. If the optional loader argument is provided,
the loader will be invoked to load the model into memory. Otherwise the
method will call `safetensors.torch.load_file()` `torch.load()` (with a
pickle scan), or `from_pretrained()` as appropriate to the path type.
Be aware that the `LoadedModelWithoutConfig` object differs from
`LoadedModel` by having no `config` attribute.
Here is an example of usage:
```
def invoke(self, context: InvocatinContext) -> ImageOutput:
model_path = Path('/opt/models/RealESRGAN_x4plus.pth')
loadnet = context.models.load_local_model(model_path)
with loadnet as loadnet_model:
upscaler = RealESRGAN(loadnet=loadnet_model,...)
```
---
2. **`load_remote_model(source: str | AnyHttpUrl, loader:
Optional[Callable[[Path], AnyModel]] = None) ->
LoadedModelWithoutConfig`**
*Load the model located at the indicated URL or repo_id.*
This is similar to `load_local_model()` but it accepts either a
HugginFace repo_id (as a string), or a URL. The model's file(s) will be
downloaded to `models/.download_cache` and then loaded, returning a
```
def invoke(self, context: InvocatinContext) -> ImageOutput:
model_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
loadnet = context.models.load_remote_model(model_url)
with loadnet as loadnet_model:
upscaler = RealESRGAN(loadnet=loadnet_model,...)
```
---
3. **`download_and_cache_model( source: str | AnyHttpUrl, access_token:
Optional[str] = None, timeout: Optional[int] = 0) -> Path`**
Download the model file located at source to the models cache and return
its Path. This will check `models/.download_cache` for the desired model
file and download it from the indicated source if not already present.
The local Path to the downloaded file is then returned.
---
## Other Changes
This PR performs a migration, in which it renames `models/.cache` to
`models/.convert_cache`, and migrates previously-downloaded ESRGAN,
openpose, DepthAnything and Lama inpaint models from the `models/core`
directory into `models/.download_cache`.
There are a number of legacy model files in `models/core`, such as
GFPGAN, which are no longer used. This PR deletes them and tidies up the
`models/core` directory.
## Related Issues / Discussions
I have systematically replaced all the calls to
`download_with_progress_bar()`. This function is no longer used
elsewhere and has been removed.
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
I have added unit tests for the three new calls. You may test that the
`load_and_cache_model()` call is working by running the upscaler within
the web app. On first try, you will see the model file being downloaded
into the models `.cache` directory. On subsequent tries, the model will
either load from RAM (if it hasn't been displaced) or will be loaded
from the filesystem.
<!--WHEN APPLICABLE: Describe how we can test the changes in this PR.-->
## Merge Plan
Squash merge when approved.
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->
## Checklist
- [X] _The PR has a short but descriptive title, suitable for a
changelog_
- [X] _Tests added / updated (if applicable)_
- [X] _Documentation added / updated (if applicable)_
## Summary
I've started working towards a better tiled upscaling implementation. It
is going to require some refactoring of `DenoiseLatentsInvocation`. As a
first step, this PR splits up all of the invocations in latent.py into
their own files. That file had become a bit of a dumping ground - it
should be a bit more manageable to work with now.
This PR just re-organizes the code. There should be no functional
changes.
## QA Instructions
I've done some light smoke testing. I'll do some more before merging.
The main risk is that I missed a broken import, or some other copy-paste
error.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_: N/A
- [x] _Documentation added / updated (if applicable)_: N/A
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* do not save original weights if there is a CPU copy of state dict
* Update invokeai/backend/model_manager/load/load_base.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* documentation fixes added during penultimate review
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
Create intermediary nanostores for values required by the event handlers. This allows the event handlers to be purely imperative, with no reactivity: instead of recreating/setting the handlers when a dependent piece of state changes, we use nanostores' imperative API to access dependent state.
For example, some handlers depend on brush size. If we used the standard declarative `useSelector` API, we'd need to recreate the event handler callback each time the brush size changed. This can be costly.
An intermediate `$brushSize` nanostore is set in a `useLayoutEffect()`, which responds to changes to the redux store. Then, in the event handler, we use the imperative API to access the brush size: `$brushSize.get()`.
This change allows the event handler logic to be shared with the pending canvas v2, and also more easily tested. It's a noticeable perf improvement, too, especially when changing brush size.
Currently translated at 98.5% (1243 of 1261 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1243 of 1261 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1225 of 1243 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1225 of 1243 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
- Pass the seed from `latents_a` to the output latents. Fixed an issue where using `BlendLatentsInvocation` could result in different outputs during denoising even when the alpha or slerp weight was 0.
## Explanation
`LatentsField` has an optional `seed` field. During denoising, if this `seed` field is not present, we **fall back to 0 for the seed**. The seed is used during denoising in a few ways:
1. Initializing the scheduler.
The seed is used in two places in `invokeai/app/invocations/latent.py`.
The `get_scheduler()` utility function has special handling for `DPMSolverSDEScheduler`, which appears to need a seed for deterministic outputs.
`DenoiseLatentsInvocation.init_scheduler()` has special handling for schedulers that accept a generator - the generator needs to be seeded in a particular way. At the time of this commit, these are the Invoke-supported schedulers that need this seed:
- DDIMScheduler
- DDPMScheduler
- DPMSolverMultistepScheduler
- EulerAncestralDiscreteScheduler
- EulerDiscreteScheduler
- KDPM2AncestralDiscreteScheduler
- LCMScheduler
- TCDScheduler
2. Adding noise during inpainting.
If a mask is used for denoising, and we are not using an inpainting model, we add noise to the unmasked area. If, for some reason, we have a mask but no noise, the seed is used to add noise.
I wonder if we should instead assert that if a mask is provided, we also have noise.
This is done in `invokeai/backend/stable_diffusion/diffusers_pipeline.py` in `StableDiffusionGeneratorPipeline.latents_from_embeddings()`.
When we create noise to be used in denoising, we are expected to set `LatentsField.seed` to the seed used to create the noise. This introduces some awkwardness when we manipulate any "latents" that will be used for denoising. We have to pass the seed along for every operation.
If the wrong seed or no seed is passed along, we can get unexpected outputs during denoising. One notable case relates to blending latents (slerping tensors).
If we slerp two noise tensors (`LatentsField`s) _without_ passing along the seed from the source latents, when we denoise with a seed-dependent scheduler*, the schedulers use the fallback seed of 0 and we get the wrong output. This is most obvious when slerping with a weight of 0, in which case we expect the exact same output after denoising.
*It looks like only the DPMSolver* schedulers are affected, but I haven't tested all of them.
Passing the seed along in the output fixes this issue.
This required some minor reworking of of the logic to recall multiple items. I split this into a utility function that includes some special handling for concat.
Closes#6478
When the model in metadata's key no longer exists, fall back to fetching by name, base and type. This was the intention all along but the logic was never put in place.
- Any mypy issues are a misconfiguration of mypy
- Use simple conditionals instead of ternaries
- Consistent & standards-compliant docstring formatting
- Use `dict` instead of `typing.Dict`
It doesn't make sense to allow context menu here, because the context menu will technically be on a div and not an image - there won't be any image options there.
## Summary
Fix some issues with openapi schema generation. See commits for details.
## Related Issues / Discussions
https://discord.com/channels/1020123559063990373/1049495067846524939/1245141831394529352
## QA Instructions
App should work, workflows should work.
## Merge Plan
n/a
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
Some tech debt related to dynamic pydantic schemas for invocations became problematic. Including the invocations and results in the event schemas was breaking pydantic's handling of ref schemas. I don't really understand why - I think it's a pydantic bug in a remote edge case that we are hitting.
After many failed attempts I landed on this implementation, which is actually much tidier than what was in there before.
- Create pydantic-enabled types for `AnyInvocation` and `AnyInvocationOutput` and use these in place of the janky dynamic unions. Actually, they are kinda the same, but better encapsulated. Use these in `Graph`, `GraphExecutionState`, `InvocationEventBase` and `InvocationCompleteEvent`.
- Revise the custom openapi function to work with the new models.
- Split out the custom openapi function to a separate file. Add a `post_transform` callback so consumers can customize the output schema.
- Update makefile scripts.
## Summary
- Updated the documentation for `TextualInversionManager`
- Updated the `self.tokenizer.model_max_length` access to work with the
latest transformers version. Thanks to @skunkworxdark for looking into
this here:
https://github.com/invoke-ai/InvokeAI/issues/6445#issuecomment-2133098342
## Related Issues / Discussions
Closes#6445
## QA Instructions
I tested with `transformers==4.41.1`, and compared the results against a
recent InvokeAI version before updating tranformers - no change, as
expected.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
This is required to get these event fields to deserialize correctly. If omitted, pydantic uses `BaseInvocation`/`BaseInvocationOutput`, which is not correct.
This is similar to the workaround in the `Graph` and `GraphExecutionState` classes where we need to fanagle pydantic with manual validation handling.
Note about the huge diff: I had a different version of pydantic installed at some point, which slightly altered a _ton_ of schema components. This typegen was done on the correct version of pydantic and un-does those alterations, in addition to the intentional changes to event models.
There's no longer any need for session-scoped events now that we have the session queue. Session started/completed/canceled map 1-to-1 to queue item status events, but queue item status events also have an event for failed state.
We can simplify queue and processor handling substantially by removing session events and instead using queue item events.
- Remove the session-scoped events entirely.
- Remove all event handling from session queue. The processor still needs to respond to some events from the queue: `QueueClearedEvent`, `BatchEnqueuedEvent` and `QueueItemStatusChangedEvent`.
- Pass an `is_canceled` callback to the invocation context instead of the cancel event
- Update processor logic to ensure the local instance of the current queue item is synced with the instance in the database. This prevents race conditions and ensures lifecycle callback do not get stale callbacks.
- Update docstrings and comments
- Add `complete_queue_item` method to session queue service as an explicit way to mark a queue item as successfully completed. Previously, the queue listened for session complete events to do this.
Closes#6442
- Restore calculation of step percentage but in the backend instead of client
- Simplify signatures for denoise progress event callbacks
- Clean up `step_callback.py` (types, do not recreate constant matrix on every step, formatting)
We don't need to use the payload schema registry. All our events are dispatched as pydantic models, which are already validated on instantiation.
We do want to add all events to the OpenAPI schema, and we referred to the payload schema registry for this. To get all events, add a simple helper to EventBase. This is functionally identical to using the schema registry.
The model loader emits events. During testing, it doesn't have access to a fully-mocked events service, so the test fails when attempting to call a nonexistent method. There was a check for this previously, but I accidentally removed it. Restored.
- Remove ABCs, they do not work well with pydantic
- Remove the event type classvar - unused
- Remove clever logic to require an event name - we already get validation for this during schema registration.
- Rename event bases to all end in "Base"
Our events handling and implementation has a couple pain points:
- Adding or removing data from event payloads requires changes wherever the events are dispatched from.
- We have no type safety for events and need to rely on string matching and dict access when interacting with events.
- Frontend types for socket events must be manually typed. This has caused several bugs.
`fastapi-events` has a neat feature where you can create a pydantic model as an event payload, give it an `__event_name__` attr, and then dispatch the model directly.
This allows us to eliminate a layer of indirection and some unpleasant complexity:
- Event handler callbacks get type hints for their event payloads, and can use `isinstance` on them if needed.
- Event payload construction is now the responsibility of the event itself (a pydantic model), not the service. Every event model has a `build` class method, encapsulating this logic. The build methods are provided as few args as possible. For example, `InvocationStartedEvent.build()` gets the invocation instance and queue item, and can choose the data it wants to include in the event payload.
- Frontend event types may be autogenerated from the OpenAPI schema. We use the payload registry feature of `fastapi-events` to collect all payload models into one place, making it trivial to keep our schema and frontend types in sync.
This commit moves the backend over to this improved event handling setup.
* avoid copying model back from cuda to cpu
* handle models that don't have state dicts
* add assertions that models need a `device()` method
* do not rely on torch.nn.Module having the device() method
* apply all patches after model is on the execution device
* fix model patching in latents too
* log patched tokenizer
* closes#6375
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Show error toasts on queue item error events instead of invocation error events. This allows errors that occurred outside node execution to be surfaced to the user.
The error description component is updated to show the new error message if available. Commercial handling is retained, but local now uses the same component to display the error message itself.
I had set the cancel event at some point during troubleshooting an unrelated issue. It seemed logical that it should be set there, and didn't seem to break anything. However, this is not correct.
The cancel event should not be set in response to a queue status change event. Doing so can cause a race condition when nodes are executed very quickly.
It's possible that a previously-executed session's queue item status change event is handled after the next session starts executing. The cancel event is set and the session runner sees it aborting the session run early.
In hindsight, it doesn't make sense to set the cancel event here either. It should be set in response to user action, e.g. the user cancelled the session or cleared the queue (which implicitly cancels the current session). These events actually trigger the queue item status changed event, so if we set the cancel event here, we'd be setting it twice per cancellation.
There's a race condition where a canceled session may emit a progress event or two after it's been canceled, and the progress image isn't cleared out.
To resolve this, the system slice tracks canceled session ids. When a progress event comes in, we check the cancellations and skip setting the progress if canceled.
- Add handling for new error columns `error_type`, `error_message`, `error_traceback`.
- Update queue item model to include the new data. The `error_traceback` field has an alias of `error` for backwards compatibility.
- Add `fail_queue_item` method. This was previously handled by `cancel_queue_item`. Splitting this functionality makes failing a queue item a bit more explicit. We also don't need to handle multiple optional error args.
-
We were not handling node preparation errors as node errors before. Here's the explanation, copied from a comment that is no longer required:
---
TODO(psyche): Sessions only support errors on nodes, not on the session itself. When an error occurs outside
node execution, it bubbles up to the processor where it is treated as a queue item error.
Nodes are pydantic models. When we prepare a node in `session.next()`, we set its inputs. This can cause a
pydantic validation error. For example, consider a resize image node which has a constraint on its `width`
input field - it must be greater than zero. During preparation, if the width is set to zero, pydantic will
raise a validation error.
When this happens, it breaks the flow before `invocation` is set. We can't set an error on the invocation
because we didn't get far enough to get it - we don't know its id. Hence, we just set it as a queue item error.
---
This change wraps the node preparation step with exception handling. A new `NodeInputError` exception is raised when there is a validation error. This error has the node (in the state it was in just prior to the error) and an identifier of the input that failed.
This allows us to mark the node that failed preparation as errored, correctly making such errors _node_ errors and not _processor_ errors. It's much easier to diagnose these situations. The error messages look like this:
> Node b5ac87c6-0678-4b8c-96b9-d215aee12175 has invalid incoming input for height
Some of the exception handling logic is cleaned up.
- Use protocol to define callbacks, this allows them to have kwargs
- Shuffle the profiler around a bit
- Move `thread_limit` and `polling_interval` to `__init__`; `start` is called programmatically and will never get these args in practice
- Add `OnNodeError` and `OnNonFatalProcessorError` callbacks
- Move all session/node callbacks to `SessionRunner` - this ensures we dump perf stats before resetting them and generally makes sense to me
- Remove `complete` event from `SessionRunner`, it's essentially the same as `OnAfterRunSession`
- Remove extraneous `next_invocation` block, which would treat a processor error as a node error
- Simplify loops
- Add some callbacks for testing, to be removed before merge
This query is only subscribed-to in the `QueueItemDetail` component - when is rendered only when the user clicks on a queue item in the queue. Invalidating this tag instead of optimistically updating it won't cause any meaningful change to network traffic.
The session is never updated in the queue after it is first enqueued. As a result, the queue detail view in the frontend never never updates and the session itself doesn't show outputs, execution graph, etc.
We need a new method on the queue service to update a queue item's session, then call it before updating the queue item's status.
Queue item status may be updated via a session-type event _or_ queue-type event. Adding the updated session to all these events is a hairy - simpler to just update the session before we do anything that could trigger a queue item status change event:
- Before calling `emit_session_complete` in the processor (handles session error, completed and cancel events and the corresponding queue events)
- Before calling `cancel_queue_item` in the processor (handles another way queue items can be canceled, outside the session execution loop)
When serializing the session, both in the new service method and the `get_queue_item` endpoint, we need to use `exclude_none=True` to prevent unexpected validation errors.
## Summary
TIL if you add `undefined` to a form data object, it gets stringified to
`'undefined'`. Whoops!
## Related Issues / Discussions
n/a
## QA Instructions
n/a
## Merge Plan
n/a
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
Currently translated at 98.5% (1210 of 1228 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1206 of 1224 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1204 of 1222 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
## Summary
Notes nodes used some overly-strict redux selectors. The selectors are
now more chill. Also fixed an issue where you couldn't edit a notes node
title.
Found another class of error related to the overly strict reducers that
caused errors when loading a workflow that had missing templates. Fixed
this with fallback wrapper component, works like an error boundary when
a template isn't found.
## Related Issues / Discussions
https://discord.com/channels/1020123559063990373/1149506274971631688/1242256425527545949
## QA Instructions
- Add a notes node to a workflow. Edit the notes title.
- Load a workflow that has nodes that aren't installed. Should get a
fallback UI for each missing node.
- Load a workflow that references a node with different inputs than are
in the template - like an old version of a node. Should get a fallback
field warning for both missing templates, or missing inputs.
## Merge Plan
n/a
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
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.
Canvas images are saved by uploading a blob generated from the HTML canvas element. This means the existing metadata handling, inside the graph execution engine, is not available.
To save metadata to canvas images, we need to provide it when uploading that blob.
The upload route now has a `metadata` body param. If this is provided, we use it over any metadata embedded in the image.
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.
Graph, metadata and workflow all take stringified JSON only. This makes the API consistent and means we don't need to do a round-trip of pydantic parsing when handling this data.
It also prevents a failure mode where an uploaded image's metadata, workflow or graph are old and don't match the current schema.
As before, the frontend does strict validation and parsing when loading these values.
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.
Pending:
- Move model install calls into model manager and create passthrus in invocation_context.
- Consider splitting load_model_from_url() into a call to get the path and a call to load the path.
Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
[Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
| **For users looking for a locally installed, self-hosted and self-managed service** | **For users or teams looking for a cloud-hosted, fully managed service** |
| - Free to use under a commercially-friendly license | - Monthly subscription fee with three different plan levels |
| - Download and install on compatible hardware | - Offers additional benefits, including multi-user support, improved model training, and more |
| - Includes all core studio features: generate, refine, iterate on images, and build workflows | - Hosted in the cloud for easy, secure model access and scalability |
| Quick Start -> [Installation and Updates][installation docs] | More Information -> [www.invoke.com/pricing](https://www.invoke.com/pricing) |
<div align="center">
| [Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs] |
</div>
## Quick Start
@@ -37,6 +49,33 @@ Invoke is a leading creative engine built to empower professionals and enthusias
More detail, including hardware requirements and manual install instructions, are available in the [installation documentation][installation docs].
## Docker Container
We publish official container images in Github Container Registry: https://github.com/invoke-ai/InvokeAI/pkgs/container/invokeai. Both CUDA and ROCm images are available. Check the above link for relevant tags.
> [!IMPORTANT]
> Ensure that Docker is set up to use the GPU. Refer to [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] documentation.
### Generate!
Run the container, modifying the command as necessary:
```bash
docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
```
Then open `http://localhost:9090` and install some models using the Model Manager tab to begin generating.
For ROCm, add `--device /dev/kfd --device /dev/dri` to the `docker run` command.
### Persist your data
You will likely want to persist your workspace outside of the container. Use the `--volume /home/myuser/invokeai:/invokeai` flag to mount some local directory (using its **absolute** path) to the `/invokeai` path inside the container. Your generated images and models will reside there. You can use this directory with other InvokeAI installations, or switch between runtime directories as needed.
### DIY
Build your own image and customize the environment to match your needs using our `docker-compose` stack. See [README.md](./docker/README.md) in the [docker](./docker) directory.
## Troubleshooting, FAQ and Support
Please review our [FAQ][faq] for solutions to common installation problems and other issues.
All commands should be run within the `docker` directory: `cd docker`
First things first:
## Quickstart :rocket:
- Ensure that Docker can use your [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] GPU.
- This document assumes a Linux system, but should work similarly under Windows with WSL2.
- We don't recommend running Invoke in Docker on macOS at this time. It works, but very slowly.
On a known working Linux+Docker+CUDA (Nvidia) system, execute `./run.sh` in this directory. It will take a few minutes - depending on your internet speed - to install the core models. Once the application starts up, open `http://localhost:9090` in your browser to Invoke!
## Quickstart
For more configuration options (using an AMD GPU, custom root directory location, etc): read on.
No `docker compose`, no persistence, single command, using the official images:
## Detailed setup
**CUDA (NVIDIA GPU):**
```bash
docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
```
**ROCm (AMD GPU):**
```bash
docker run --device /dev/kfd --device /dev/dri --publish 9090:9090 ghcr.io/invoke-ai/invokeai:main-rocm
```
Open `http://localhost:9090` in your browser once the container finishes booting, install some models, and generate away!
### Data persistence
To persist your generated images and downloaded models outside of the container, add a `--volume/-v` flag to the above command, e.g.:
```bash
docker run --volume /some/local/path:/invokeai {...etc...}
```
`/some/local/path/invokeai` will contain all your data.
It can *usually* be reused between different installs of Invoke. Tread with caution and read the release notes!
## Customize the container
The included `run.sh` script is a convenience wrapper around `docker compose`. It can be helpful for passing additional build arguments to `docker compose`. Alternatively, the familiar `docker compose` commands work just as well.
```bash
cd docker
cp .env.sample .env
# edit .env to your liking if you need to; it is well commented.
./run.sh
```
It will take a few minutes to build the image the first time. Once the application starts up, open `http://localhost:9090` in your browser to invoke!
>[!TIP]
>When using the `run.sh` script, the container will continue running after Ctrl+C. To shut it down, use the `docker compose down` command.
## Docker setup in detail
#### Linux
1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
1. Ensure buildkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
- The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
- The deprecated `docker-compose` (hyphenated) CLI probably won't work. Update to a recent version.
3. Ensure docker daemon is able to access the GPU.
-You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
> You'll be better off installing Invoke directly on your system, because Docker can not use the GPU on macOS.
If you are still reading:
1. Ensure Docker has at least 16GB RAM
2. Enable VirtioFS for file sharing
3. Enable `docker compose` V2 support
This is done via Docker Desktop preferences
This is done via Docker Desktop preferences.
### Configure Invoke environment
### Configure the Invoke Environment
1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
a. the desired location of the InvokeAI runtime directory, or
b. an existing, v3.0.0 compatible runtime directory.
1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to the desired location of the InvokeAI runtime directory. It may be an existing directory from a previous installation (post 4.0.0).
1. Execute `run.sh`
The image will be built automatically if needed.
The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. The runtime directory will be populated with the base configs and models necessary to start generating.
The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. Navigate to the Model Manager tab and install some models before generating.
### Use a GPU
@@ -43,9 +90,9 @@ The runtime directory (holding models and outputs) will be created in the locati
- WSL2 is *required* for Windows.
- only `x86_64` architecture is supported.
The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker/NVIDIA/AMD documentation for the most up-to-date instructions for using your GPU with Docker.
To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file.
To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file before running `./run.sh`.
## Customize
@@ -59,30 +106,12 @@ Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The defa
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000
GPU_DRIVER=nvidia
GPU_DRIVER=cuda
```
Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
Any environment variables supported by InvokeAI can be set here. See the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
## Even Moar Customizing!
---
See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
### Reconfigure the runtime directory
Can be used to download additional models from the supported model list
In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
@@ -117,13 +117,13 @@ Stateless fields do not store their value in the node, so their field instances
"Custom" fields will always be treated as stateless fields.
##### Collection and Scalar Fields
##### Single and Collection Fields
Field types have a name and two flags which may identify it as a **collection** or **collection or scalar** field.
Field types have a name and cardinality property which may identify it as a **SINGLE**, **COLLECTION** or **SINGLE_OR_COLLECTION** field.
If a field is annotated in python as a list, its field type is parsed and flagged as a **collection** type (e.g. `list[int]`).
If it is annotated as a union of a type and list, the type will be flagged as a **collection or scalar** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
-If a field is annotated in python as a singular value or class, its field type is parsed as a **SINGLE** type (e.g. `int`, `ImageField`, `str`).
- If a field is annotated in python as a list, its field type is parsed as a **COLLECTION** type (e.g. `list[int]`).
-If it is annotated as a union of a type and list, the type will be parsed as a **SINGLE_OR_COLLECTION** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
## Implementation
@@ -173,8 +173,7 @@ Field types are represented as structured objects:
@@ -186,7 +185,7 @@ There are 4 general cases for field type parsing.
When a field is annotated as a primitive values (e.g. `int`, `str`, `float`), the field type parsing is fairly straightforward. The field is represented by a simple OpenAPI **schema object**, which has a `type` property.
We create a field type name from this `type` string (e.g. `string` -> `StringField`).
We create a field type name from this `type` string (e.g. `string` -> `StringField`). The cardinality is `"SINGLE"`.
##### Complex Types
@@ -200,13 +199,13 @@ We need to **dereference** the schema to pull these out. Dereferencing may requi
When a field is annotated as a list of a single type, the schema object has an `items` property. They may be a schema object or reference object and must be parsed to determine the item type.
We use the item type for field type name, adding `isCollection: true` to the field type.
We use the item type for field type name. The cardinality is `"COLLECTION"`.
##### Collection or Scalar Types
##### Single or Collection Types
When a field is annotated as a union of a type and list of that type, the schema object has an `anyOf` property, which holds a list of valid types for the union.
After verifying that the union has two members (a type and list of the same type), we use the type for field type name, adding `isCollectionOrScalar: true` to the field type.
After verifying that the union has two members (a type and list of the same type), we use the type for field type name, with cardinality `"SINGLE_OR_COLLECTION"`.
@@ -165,7 +165,7 @@ Additionally, each section can be expanded with the "Show Advanced" button in o
There are several ways to install IP-Adapter models with an existing InvokeAI installation:
1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.
2. Through the Model Manager UI with models from the *Tools* section of [models.invoke.ai](https://models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.
3.**Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder.
@@ -154,6 +154,18 @@ This is caused by an invalid setting in the `invokeai.yaml` configuration file.
Check the [configuration docs] for more detail about the settings and how to specify them.
## `ModuleNotFoundError: No module named 'controlnet_aux'`
`controlnet_aux` is a dependency of Invoke and appears to have been packaged or distributed strangely. Sometimes, it doesn't install correctly. This is outside our control.
If you encounter this error, the solution is to remove the package from the `pip` cache and re-run the Invoke installer so a fresh, working version of `controlnet_aux` can be downloaded and installed:
- Run the Invoke launcher
- Choose the developer console option
- Run this command: `pip cache remove controlnet_aux`
- Close the terminal window
- Download and run the [installer](https://github.com/invoke-ai/InvokeAI/releases/latest), selecting your current install location
## Out of Memory Issues
The models are large, VRAM is expensive, and you may find yourself
@@ -184,6 +196,22 @@ tips to reduce the problem:
=== "12GB VRAM GPU"
This should be sufficient to generate larger images up to about 1280x1280.
## Checkpoint Models Load Slowly or Use Too Much RAM
The difference between diffusers models (a folder containing multiple
subfolders) and checkpoint models (a file ending with .safetensors or
.ckpt) is that InvokeAI is able to load diffusers models into memory
incrementally, while checkpoint models must be loaded all at
once. With very large models, or systems with limited RAM, you may
experience slowdowns and other memory-related issues when loading
checkpoint models.
To solve this, go to the Model Manager tab (the cube), select the
checkpoint model that's giving you trouble, and press the "Convert"
button in the upper right of your browser window. This will conver the
checkpoint into a diffusers model, after which loading should be
@@ -20,7 +20,7 @@ When you generate an image using text-to-image, multiple steps occur in latent s
4. The VAE decodes the final latent image from latent space into image space.
Image-to-image is a similar process, with only step 1 being different:
1. The input image is encoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how may noise steps are added, and the amount of noise added at each step. A Denoising Strength of 0 means there are 0 steps and no noise added, resulting in an unchanged image, while a Denoising Strength of 1 results in the image being completely replaced with noise and a full set of denoising steps are performance. The process is then the same as steps 2-4 in the text-to-image process.
1. The input image is encoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how many noise steps are added, and the amount of noise added at each step. A Denoising Strength of 0 means there are 0 steps and no noise added, resulting in an unchanged image, while a Denoising Strength of 1 results in the image being completely replaced with noise and a full set of denoising steps are performance. The process is then the same as steps 2-4 in the text-to-image process.
Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
@@ -10,7 +10,7 @@ InvokeAI is distributed as a python package on PyPI, installable with `pip`. The
### Requirements
Before you start, go through the [installation requirements].
Before you start, go through the [installation requirements](./INSTALL_REQUIREMENTS.md).
### Installation Walkthrough
@@ -79,7 +79,7 @@ Before you start, go through the [installation requirements].
1. Install the InvokeAI Package. The base command is `pip install InvokeAI --use-pep517`, but you may need to change this depending on your system and the desired features.
- You may need to provide an [extra index URL]. Select your platform configuration using [this tool on the PyTorch website]. Copy the `--extra-index-url` string from this and append it to your install command.
- You may need to provide an [extra index URL](https://pip.pypa.io/en/stable/cli/pip_install/#cmdoption-extra-index-url). Select your platform configuration using [this tool on the PyTorch website](https://pytorch.org/get-started/locally/). Copy the `--extra-index-url` string from this and append it to your install command.
!!! example "Install with an extra index URL"
@@ -116,4 +116,4 @@ Before you start, go through the [installation requirements].
!!! warning
If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root_dir \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.
If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.
We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md),
because Docker containers can not access the GPU on macOS.
!!! warning "AMD GPU Users"
Container support for AMD GPUs has been reported to work by the community, but has not received
extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and
use the `build.sh` script to build the image for this to take effect at build time.
Docker can not access the GPU on macOS, so your generation speeds will be slow. [Install InvokeAI](INSTALLATION.md) instead.
!!! tip "Linux and Windows Users"
For optimal performance, configure your Docker daemon to access your machine's GPU.
Configure Docker to access your machine's GPU.
Docker Desktop on Windows [includes GPU support](https://www.docker.com/blog/wsl-2-gpu-support-for-docker-desktop-on-nvidia-gpus/).
Linux users should install and configure the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
## Why containers?
They provide a flexible, reliable way to build and deploy InvokeAI.
See [Processes](https://12factor.net/processes) under the Twelve-Factor App
methodology for details on why running applications in such a stateless fashion is important.
The container is configured for CUDA by default, but can be built to support AMD GPUs
by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time.
Developers on Apple silicon (M1/M2/M3): You
[can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
and performance is reduced compared with running it directly on macOS but for
development purposes it's fine. Once you're done with development tasks on your
laptop you can build for the target platform and architecture and deploy to
another environment with NVIDIA GPUs on-premises or in the cloud.
Linux users should follow the [NVIDIA](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) or [AMD](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html) documentation.
## TL;DR
This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options.
Ensure your Docker setup is able to use your GPU. Then:
```bash
docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
```
Once the container starts up, open http://localhost:9090 in your browser, install some models, and start generating.
## Build-It-Yourself
All the docker materials are located inside the [docker](https://github.com/invoke-ai/InvokeAI/tree/main/docker) directory in the Git repo.
```bash
# docker compose commands should be run from the `docker` directory
cd docker
cp .env.sample .env
docker compose up
```
## Installation in a Linux container (desktop)
We also ship the `run.sh` convenience script. See the `docker/README.md` file for detailed instructions on how to customize the docker setup to your needs.
### Prerequisites
@@ -58,18 +45,9 @@ Preferences, Resources, Advanced. Increase the CPUs and Memory to avoid this
[Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
increase Swap and Disk image size too.
#### Get a Huggingface-Token
Besides the Docker Agent you will need an Account on
[huggingface.co](https://huggingface.co/join).
After you succesfully registered your account, go to
a token and copy it, since you will need in for the next step.
### Setup
Set up your environmnent variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
Set up your environment variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
@@ -103,10 +81,9 @@ Once the container starts up (and configures the InvokeAI root directory if this
## Troubleshooting / FAQ
- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
and you may have cloned this repository before the issue was fixed. To solve this, please change
the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
- A: Your `docker-entrypoint.sh` might have has Windows (CRLF) line endings, depending how you cloned the repository.
To solve this, change the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
(`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
Finally, you may delete `docker-entrypoint.sh` followed by `git pull; git checkout docker/docker-entrypoint.sh`
to reset the file to its most recent version.
For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)
For more information on this issue, see [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)
heading="There was an problem installing this model."
message='Please use the Model Manager directly to install this model. If the issue persists, ask for help on <a href="https://discord.gg/ZmtBAhwWhy">discord</a>.'
@@ -61,11 +60,13 @@ class Classification(str, Enum, metaclass=MetaEnum):
- `Stable`: The invocation, including its inputs/outputs and internal logic, is stable. You may build workflows with it, having confidence that they will not break because of a change in this invocation.
- `Beta`: The invocation is not yet stable, but is planned to be stable in the future. Workflows built around this invocation may break, but we are committed to supporting this invocation long-term.
- `Prototype`: The invocation is not yet stable and may be removed from the application at any time. Workflows built around this invocation may break, and we are *not* committed to supporting this invocation.
- `Deprecated`: The invocation is deprecated and may be removed in a future version.
"""
Stable="stable"
Beta="beta"
Prototype="prototype"
Deprecated="deprecated"
classUIConfigBase(BaseModel):
@@ -80,7 +81,7 @@ class UIConfigBase(BaseModel):
version:str=Field(
description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
)
node_pack:Optional[str]=Field(default=None,description="Whether or not this is a custom node")
node_pack:str=Field(description="The node pack that this node belongs to, will be 'invokeai' for built-in nodes")
lora_weight="The weight at which the LoRA is applied to each model"
compel_prompt="Prompt to be parsed by Compel to create a conditioning tensor"
raw_prompt="Raw prompt text (no parsing)"
@@ -160,6 +170,7 @@ class FieldDescriptions:
fp32="Whether or not to use full float32 precision"
precision="Precision to use"
tiled="Processing using overlapping tiles (reduce memory consumption)"
vae_tile_size="The tile size for VAE tiling in pixels (image space). If set to 0, the default tile size for the model will be used. Larger tile sizes generally produce better results at the cost of higher memory usage."
detect_res="Pixel resolution for detection"
image_res="Pixel resolution for output image"
safe_mode="Whether or not to use safe mode"
@@ -170,7 +181,7 @@ class FieldDescriptions:
)
num_1="The first number"
num_2="The second number"
mask="The mask to use for the operation"
denoise_mask="A mask of the region to apply the denoising process to. Values of 0.0 represent the regions to be fully denoised, and 1.0 represent the regions to be preserved."
board="The board to save the image to"
image="The image to process"
tile_size="Tile size"
@@ -228,6 +239,12 @@ class ColorField(BaseModel):
return(self.r,self.g,self.b,self.a)
classFluxConditioningField(BaseModel):
"""A conditioning tensor primitive value"""
conditioning_name:str=Field(description="The name of conditioning tensor")
classConditioningField(BaseModel):
"""A conditioning tensor primitive value"""
@@ -239,6 +256,31 @@ class ConditioningField(BaseModel):
)
classBoundingBoxField(BaseModel):
"""A bounding box primitive value."""
x_min:int=Field(ge=0,description="The minimum x-coordinate of the bounding box (inclusive).")
x_max:int=Field(ge=0,description="The maximum x-coordinate of the bounding box (exclusive).")
y_min:int=Field(ge=0,description="The minimum y-coordinate of the bounding box (inclusive).")
y_max:int=Field(ge=0,description="The maximum y-coordinate of the bounding box (exclusive).")
score:Optional[float]=Field(
default=None,
ge=0.0,
le=1.0,
description="The score associated with the bounding box. In the range [0, 1]. This value is typically set "
"when the bounding box was produced by a detector and has an associated confidence score.",
)
@model_validator(mode="after")
defcheck_coords(self):
ifself.x_min>self.x_max:
raiseValueError(f"x_min ({self.x_min}) is greater than x_max ({self.x_max}).")
ifself.y_min>self.y_max:
raiseValueError(f"y_min ({self.y_min}) is greater than y_max ({self.y_max}).")
returnself
classMetadataField(RootModel[dict[str,Any]]):
"""
Pydantic model for metadata with custom root of type dict[str, Any].
width:int=InputField(default=1024,multiple_of=16,description="Width of the generated image.")
height:int=InputField(default=1024,multiple_of=16,description="Height of the generated image.")
num_steps:int=InputField(
default=4,description="Number of diffusion steps. Recommended values are schnell: 4, dev: 50."
)
guidance:float=InputField(
default=4.0,
description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
)
seed:int=InputField(default=0,description="Randomness seed for reproducibility.")
model:SegmentAnythingModelKey=InputField(description="The Segment Anything model to use.")
image:ImageField=InputField(description="The image to segment.")
bounding_boxes:list[BoundingBoxField]=InputField(description="The bounding boxes to prompt the SAM model with.")
apply_polygon_refinement:bool=InputField(
description="Whether to apply polygon refinement to the masks. This will smooth the edges of the masks slightly and ensure that each mask consists of a single closed polygon (before merging).",
@@ -85,11 +85,13 @@ class InvokeAIAppConfig(BaseSettings):
log_tokenization: Enable logging of parsed prompt tokens.
patchmatch: Enable patchmatch inpaint code.
models_dir: Path to the models directory.
convert_cache_dir: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.
convert_cache_dir: Path to the converted models cache directory (DEPRECATED, but do not delete because it is needed for migration from previous versions).
download_cache_dir: Path to the directory that contains dynamically downloaded models.
legacy_conf_dir: Path to directory of legacy checkpoint config files.
db_dir: Path to InvokeAI databases directory.
outputs_dir: Path to directory for outputs.
custom_nodes_dir: Path to directory for custom nodes.
style_presets_dir: Path to directory for style presets.
log_handlers: Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".
log_format: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.<br>Valid values: `plain`, `color`, `syslog`, `legacy`
log_level: Emit logging messages at this level or higher.<br>Valid values: `debug`, `info`, `warning`, `error`, `critical`
@@ -101,7 +103,6 @@ class InvokeAIAppConfig(BaseSettings):
profiles_dir: Path to profiles output directory.
ram: Maximum memory amount used by memory model cache for rapid switching (GB).
vram: Amount of VRAM reserved for model storage (GB).
convert_cache: Maximum size of on-disk converted models cache (GB).
lazy_offload: Keep models in VRAM until their space is needed.
log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
@@ -112,6 +113,7 @@ class InvokeAIAppConfig(BaseSettings):
force_tiled_decode: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
pil_compress_level: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
max_queue_size: Maximum number of items in the session queue.
clear_queue_on_startup: Empties session queue on startup.
allow_nodes: List of nodes to allow. Omit to allow all.
deny_nodes: List of nodes to deny. Omit to deny none.
node_cache_size: How many cached nodes to keep in memory.
@@ -146,11 +148,13 @@ class InvokeAIAppConfig(BaseSettings):
# PATHS
models_dir:Path=Field(default=Path("models"),description="Path to the models directory.")
convert_cache_dir:Path=Field(default=Path("models/.cache"),description="Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.")
convert_cache_dir:Path=Field(default=Path("models/.convert_cache"),description="Path to the converted models cache directory (DEPRECATED, but do not delete because it is needed for migration from previous versions).")
download_cache_dir:Path=Field(default=Path("models/.download_cache"),description="Path to the directory that contains dynamically downloaded models.")
legacy_conf_dir:Path=Field(default=Path("configs"),description="Path to directory of legacy checkpoint config files.")
db_dir:Path=Field(default=Path("databases"),description="Path to InvokeAI databases directory.")
outputs_dir:Path=Field(default=Path("outputs"),description="Path to directory for outputs.")
custom_nodes_dir:Path=Field(default=Path("nodes"),description="Path to directory for custom nodes.")
style_presets_dir:Path=Field(default=Path("style_presets"),description="Path to directory for style presets.")
# LOGGING
log_handlers:list[str]=Field(default=["console"],description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".')
@@ -167,9 +171,8 @@ class InvokeAIAppConfig(BaseSettings):
profiles_dir:Path=Field(default=Path("profiles"),description="Path to profiles output directory.")
# CACHE
ram:float=Field(default_factory=get_default_ram_cache_size,gt=0,description="Maximum memory amount used by memory model cache for rapid switching (GB).")
vram:float=Field(default=DEFAULT_VRAM_CACHE,ge=0,description="Amount of VRAM reserved for model storage (GB).")
convert_cache:float=Field(default=DEFAULT_CONVERT_CACHE,ge=0,description="Maximum size of on-disk converted models cache (GB).")
ram:float=Field(default_factory=get_default_ram_cache_size,gt=0,description="Maximum memory amount used by memory model cache for rapid switching (GB).")
vram:float=Field(default=DEFAULT_VRAM_CACHE,ge=0,description="Amount of VRAM reserved for model storage (GB).")
lazy_offload:bool=Field(default=True,description="Keep models in VRAM until their space is needed.")
log_memory_usage:bool=Field(default=False,description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.")
@@ -184,6 +187,7 @@ class InvokeAIAppConfig(BaseSettings):
force_tiled_decode:bool=Field(default=False,description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).")
pil_compress_level:int=Field(default=1,description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.")
max_queue_size:int=Field(default=10000,gt=0,description="Maximum number of items in the session queue.")
clear_queue_on_startup:bool=Field(default=False,description="Empties session queue on startup.")
# NODES
allow_nodes:Optional[list[str]]=Field(default=None,description="List of nodes to allow. Omit to allow all.")
@@ -298,11 +302,21 @@ class InvokeAIAppConfig(BaseSettings):
"""Path to the models directory, resolved to an absolute path.."""
returnself._resolve(self.models_dir)
@property
defstyle_presets_path(self)->Path:
"""Path to the style presets directory, resolved to an absolute path.."""
returnself._resolve(self.style_presets_dir)
@property
defconvert_cache_path(self)->Path:
"""Path to the converted cache models directory, resolved to an absolute path.."""
returnself._resolve(self.convert_cache_dir)
@property
defdownload_cache_path(self)->Path:
"""Path to the downloaded models directory, resolved to an absolute path.."""
returnself._resolve(self.download_cache_dir)
@property
defcustom_nodes_path(self)->Path:
"""Path to the custom nodes directory, resolved to an absolute path.."""
@@ -348,14 +362,14 @@ class DefaultInvokeAIAppConfig(InvokeAIAppConfig):
Some files were not shown because too many files have changed in this diff
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