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.
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
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discord. If this PR closes an issue, please use the "Closes #1234"
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## QA Instructions
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## Merge Plan
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## Checklist
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- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
## Summary
added a base prop for selectedWorkflow to allow loading a workflow on
launch
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## QA Instructions
can test by loading InvokeAIUI with a selectedWorkflow prop of the
workflow ID
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- 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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- [ ] _Tests added / updated (if applicable)_
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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)
Currently translated at 98.6% (1302 of 1320 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.6% (1294 of 1312 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
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
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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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## 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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## 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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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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Currently translated at 98.4% (1245 of 1265 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
- 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)
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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.
These simplify loading multiple LoRAs. Instead of requiring chained lora loader nodes, configure each LoRA (model & weight) with a selector, collect them, then send the collection to the collection loader to apply all of the LoRAs to the UNet/CLIP models.
The collection loaders accept a single lora or collection of loras.
This stateful class provides abstractions for building a graph. It exposes graph methods like adding and removing nodes and edges.
The methods are documented, tested, and strongly typed.
## Summary
Bump to v4.2.1
## Related Issues / Discussions
n/a
## QA Instructions
n/a
## Merge Plan
Do the release after merging.
## 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% (1192 of 1210 strings)
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Currently translated at 98.5% (1192 of 1210 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1192 of 1210 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.5% (1192 of 1210 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (1209 of 1209 strings)
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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 97.3% (1154 of 1185 strings)
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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 96.0% (1138 of 1185 strings)
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Currently translated at 98.3% (1155 of 1174 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.4% (1129 of 1147 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Make the Invoke button show a loading spinner while queueing.
The queue mutations need to be awaited else the `isLoading` state doesn't work as expected. I feel like I should understand why, but I don't...
## Summary
Do not listen for mouse events on CA and II layers (which are not
interact-able).
## Related Issues / Discussions
Closes#6331
## QA Instructions
Move a CA or II layer above a regional guidance layer. The move tool
should now work.
## 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)_
## Summary
Use translations instead of plain strings.
## Related Issues / Discussions
https://discord.com/channels/1020123559063990373/1054129386447716433/1239181243078279208
## QA Instructions
The layer select should still work.
## 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)_
## Summary
The select had a default search value, which meant it only showed
"small" as an option on first load.
## Related Issues / Discussions
n/a
## QA Instructions
- Add a CA layer
- Expand advanced
- Set processor to depth anything
- Click the model size dropdown, it should show all 3 sizes
## 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)_
This allows comboboxes for models to have more granular groupings. For example, Control Adapter models can be grouped by base model & model type.
Before:
- `SD-1`
- `SDXL`
After:
- `SD-1 / ControlNet`
- `SD-1 / T2I Adapter`
- `SDXL / ControlNet`
- `SDXL / T2I Adapter`
When a control adapter processor config is changed, if we were already processing an image, that batch is immediately canceled. This prevents the processed image from getting stuck in a weird state if you change or reset the processor at the right (err, wrong?) moment.
- Update internal state for control adapters to track processor batches, instead of just having a flag indicating if the image is processing. Add a slice migration to not break the user's existing app state.
- Update preprocessor listener with more sophisticated logic to handle canceling the batch and resetting the processed image when the config changes or is reset.
- Fixed error handling that erroneously showed "failed to queue graph" errors when an active listener instance is canceled, need to check the abort signal.
This is largely an internal change, and it should have been this way from the start - less tip-toeing around layer types. The user-facing change is when you click an IP Adapter layer, it is highlighted. That's it.
Turns out, it's more efficient to just use the bbox logic for empty mask calculations. We already track if if the bbox needs updating, so this calculation does minimal work.
The dedicated calculation wasn't able to use the bbox tracking so it ran far more often than the bbox calculation.
Removed the "fast" bbox calculation logic, bc the new logic means we are continually updating the bbox in the background - not only when the user switches to the move tool and/or selects a layer.
The bbox calculation logic is split out from the bbox rendering logic to support this.
Result - better perf overall, with the empty mask handling retained.
Mask vector data includes additive (brush, rect) shapes and subtractive (eraser) shapes. A different composite operation is used to draw a shape, depending on whether it is additive or subtractive.
This means that a mask may have vector objects, but once rendered, is _visually_ empty (fully transparent). The only way determine if a mask is visually empty is to render it and check every pixel.
When we generate and save layer metadata, these fully erased masks are still used. Generating with an empty mask is a no-op in the backend, so we want to avoid this and not pollute graphs/metadata.
Previously, we did that pixel-based when calculating the bbox, which we only did when using the move tool, and only for the selected layer.
This change introduces a simpler function to check if a mask is transparent, and if so, deletes all its objects to reset it. This allows us skip these no-op layers entirely.
This check is debounced to 300 ms, trailing edge only.
When layer metadata is stored, the layer IDs are included. When recalling the metadata, we need to assign fresh IDs, else we can end up with multiple layers with the same ID, which of course causes all sorts of issues.
- Viewer only exists on Generation tab
- Viewer defaults to open
- When clicking the Control Layers tab on the left panel, close the viewer (i.e. open the CL editor)
- Do not switch to editor when adding layers (this is handled by clicking the Control Layers tab)
- Do not open viewer when single-clicking images in gallery
- _Do_ open viewer when _double_-clicking images in gallery
- Do not change viewer state when switching between app tabs (this no longer makes sense; the viewer only exists on generation tab)
- Change the button to a drop down menu that states what you are currently doing, e.g. Viewing vs Editing
There are unresolved platform-specific issues with this component, and its utility is debatable.
Should be easy to just revert this commit to add it back in the future if desired.
There are a number of bugs with `framer-motion` that can result in sync issues with AnimatePresence and the conditionally rendered component.
You can see this if you rapidly click an accordion, occasionally it gets out of sync and is closed when it should be open.
This is a bigger problem with the viewer where the user may hold down the `z` key. It's trivial to get it to lock up.
For now, just remove the animation entirely.
Upstream issues for reference:
https://github.com/framer/motion/issues/2023https://github.com/framer/motion/issues/2618https://github.com/framer/motion/issues/2554
- Rects snap to stage edge when within a threshold (10 screen pixels)
- When mouse leaves stage, set last mousedown pos to null, preventing nonfunctional rect outlines
Partially addresses #6306.
There's a technical challenge to fully address the issue - mouse event are not fired when the mouse is outside the stage. While we could draw the rect even if the mouse leaves, we cannot update the rect's dimensions on mouse move, or complete the drawing on mouse up.
To fully address the issue, we'd need to a way to forward window events back to the stage, or at least handle window events. We can explore this later.
When invoking with control layers, we were creating and uploading the mask images on every enqueue, even when the mask didn't change. The mask image can be cached to greatly reduce the number of uploads.
With this change, we are a bit smarter about the mask images:
- Check if there is an uploaded mask image name
- If so, attempt to retrieve its DTO. Typically it will be in the RTKQ cache, so there is no network request, but it will make a network request if not cached to confirm the image actually exists on the server.
- If we don't have an uploaded mask image name, or the request fails, we go ahead and upload the generated blob
- Update the layer's state with a reference to this uploaded image for next time
- Continue as before
Any time we modify the mask (drawing/erasing, resetting the layer), we invalidate that cached image name (set it to null).
We now only upload images when we need to and generation starts faster.
- Rework styling
- Replace "CurrentImageDisplay" entirely
- Add a super short fade to reduce jarring transition
- Make the viewer a singleton component, overlaid on everything else - reduces change when switching tabs
- Works on txt2img, canvas and workflows tabs, img2img has its own side-by-side view
- In workflow editor, the is closeable only if you are in edit mode, else it's always there
- Press `i` to open
- Press `esc` to close
- Selecting an image or changing image selection opens the viewer
- When generating, if auto-switch to new image is enabled, the viewer opens when an image comes in
To support this change, I organized and restructured some tab stuff.
When recalling metadata and/or using control image dimensions, it was possible to set a width or height that was not a multiple of 8, resulting in generation failures.
Added a `clamp` option to the w/h actions to fix this. The option is used for all untrusted sources - everything except for the w/h number inputs, which clamp the values themselves.
Firefox v125.0.3 and below has a bug where `mouseenter` events are fired continually during mouse moves. The issue isn't present on FF v126.0b6 Developer Edition. It's not clear if the issue is present on FF nightly, and we're not sure if it will actually be fixed in the stable v126 release.
The control layers drawing logic relied on on `mouseenter` events to create new lines, and `mousemove` to extend existing lines. On the affected version of FF, all line extensions are turned into new lines, resulting in very poor performance, noncontiguous lines, and way-too-big internal state.
To resolve this, the drawing handling was updated to not use `mouseenter` at all. As a bonus, resolving this issue has resulted in simpler logic for drawing on the canvas.
- Add set of metadata handlers for the control layers CAs
- Use these conditionally depending on the active tab - when recalling on txt2img, the CAs go to control layers, else they go to the old CA area.
These changes were left over from the previous attempt to handle control adapters in control layers with the same logic. Control Layers are now handled totally separately, so these changes may be reverted.
There were some invalid constraints with the processors - minimum of 0 for resolution or multiple of 64 for resolution.
Made minimum 1px and no multiple ofs.
When typing in a number into the w/h number inputs, if the number is less than the step, it appears the value of 0 is used. This is unexpected; it means Chakra isn't clamping the value correctly (or maybe our wrapper isn't clamping it).
Add checks to never bail if the width or height value from the number input component is 0.
- Revise control adapter config types
- Recreate all control adapter mutations in control layers slice
- Bit of renaming along the way - typing 'RegionalGuidanceLayer' over and over again was getting tedious
Konva doesn't react to changes to window zoom/scale. If you open the tab at, say, 90%, then bump to 100%, the pixel ratio of the canvas doesn't change. This results in lower-quality renders on the canvas (generation is unaffected).
`PC_PATH_MAX` doesn't exist for (some?) external drives on macOS. We need error handling when retrieving this value.
Also added error handling for `PC_NAME_MAX` just in case. This does work for me for external drives on macOS, though.
Closes#6277
There are only a couple SDXL inpainting models, and my tests indicate they are not as good as SD1.5 inpainting, but at least we support them now.
- Add the config file. This matches what is used in A1111. The only difference from the non-inpainting SDXL config is the number of in-channels.
- Update the legacy config maps to use this config file.
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.
- Use the our adaptation of the HWC3 function with better types
- Extraction some of the util functions, name them better, add comments
- Improve type annotations
- Remove unreachable codepaths
## 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
<!--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 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)_
- Shift+C: Reset selected layer mask (same as canvas)
- Shift+D: Delete selected layer (cannot be Del, that deletes an image in gallery)
- Shift+A: Add layer (cannot be Ctrl+Shift+N, that opens a new window)
- Ctrl/Cmd+Wheel: Brush size (same as canvas)
Trying a lot of different things as I iterated, so this is smooshed into one big commit... too hard to split it now.
- Iterated on IP adapter handling and UI. Unfortunately there is an bug related to undo/redo. The IP adapter state is split across the `controlAdapters` slice and the `regionalPrompts` slice, but only the `regionalPrompts` slice supports undo/redo. If you delete the IP adapter and then undo/redo to a history state where it existed, you'll get an error. The fix is likely to merge the slices... Maybe there's a workaround.
- Iterated on UI. I think the layers are OK now.
- Removed ability to disable RP globally for now. It's enabled if you have enabled RP layers.
- Many minor tweaks and fixes.
- Keep track of whether the bbox needs to be recalculated (e.g. had lines/points added)
- Keep track of whether the bbox has eraser strokes - if yes, we need to do the full pixel-perfect bbox calculation, otherwise we can use the faster getClientRect
- Use comparison rather than Math.min/max in bbox calculation (slightly faster)
- Return `null` if no pixel data at all in bbox
Adds an additional negative conditioning using the inverted mask of the positive conditioning and the positive prompt. May be useful for mutually exclusive regions.
## Summary
Until now IP Adapter had complete control on the contents of the output.
With this PR, users are now able to select "Style Only" or "Composition
Only" to draw just the style or layout of the reference image.
Based off: https://arxiv.org/abs/2404.02733
### New IP Method Option
- `Full` - Both style and layout of the refence image are used.
- `Style Only` - Only the style of the image is used
- `Composition Only` - Only the composition of the image is used.
### Example Result
### Notes
- Supports both SDXL and SD1.5
### Testing
- Just check and test if it works as expected with all IP Adapter models
- both SDXL and SD1.5
## Merge Plan
Good to merge once tested for all edge cases.
* introduce new abstraction layer for GPU devices
* add unit test for device abstraction
* fix ruff
* convert TorchDeviceSelect into a stateless class
* move logic to select context-specific execution device into context API
* add mock hardware environments to pytest
* remove dangling mocker fixture
* fix unit test for running on non-CUDA systems
* remove unimplemented get_execution_device() call
* remove autocast precision
* Multiple changes:
1. Remove TorchDeviceSelect.get_execution_device(), as well as calls to
context.models.get_execution_device().
2. Rename TorchDeviceSelect to TorchDevice
3. Added back the legacy public API defined in `invocation_api`, including
choose_precision().
4. Added a config file migration script to accommodate removal of precision=autocast.
* add deprecation warnings to choose_torch_device() and choose_precision()
* fix test crash
* remove app_config argument from choose_torch_device() and choose_torch_dtype()
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Currently translated at 98.4% (1122 of 1140 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.4% (1120 of 1138 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.4% (1115 of 1133 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
When using refiner with a mask (i.e. inpainting), we don't have noise provided as an input to the node.
This situation uniquely hits a code path that wasn't reviewed when gradient denoising was implemented.
That code path does two things wrong:
- It lerp'd the input latents. This was fixed in 5a1f4cb1ce.
- It added noise to the latents an extra time. This is fixed in this change.
We don't need to add noise in `latents_from_embeddings` because we do it just a lines later in `AddsMaskGuidance`.
- Remove the extraneous call to `add_noise`
- Make `seed` a required arg. We never call the function without seed anyways. If we refactor this in the future, it will be clearer that we need to look at how seed is handled.
- Move the call to create the noise to a deeper conditional, just before we call `AddsMaskGuidance`. The created noise tensor is now only used in that function, no need to create it every time.
Note: Whether or not having both noise and latents as inputs on the node is correct is a separate conversation. This change just fixes the issue with the current setup.
`LatentsField` objects have an optional `seed` field. This should only be populated when the latents are noise, generated from a seed.
`DenoiseLatentsInvocation` needs a seed value for scheduler initialization. It's used in a few places, and there is some logic for determining the seed to use with a series of fallbacks:
- Use the seed from the noise (a `LatentsField` object)
- Use the seed from the latents (a `LatentsField` object - normally it won't have a seed)
- Use `0` as a final fallback
In `DenoisLatentsInvocation`, we set the seed in the `LatentsOutput`, even though the output latents are not noise.
This is normally fine, but when we use refiner, we re-use the those same latents for the refiner denoise. This causes that characteristic same-seed-fried look on the refiner pass.
Simple fix - do not set the field in the output latents.
Handful of intertwined fixes.
- Create and use helper function to reset staging area.
- Clear staging area when queue items are canceled, failed, cleared, etc. Fixes a bug where the bbox ends up offset and images are put into the wrong spot.
- Fix a number of similar bugs where canvas would "forget" it had pending generations, but they continued to generate. Canvas needs to track batches that should be displayed in it using `state.canvas.batchIds`, and this was getting cleared without actually canceling those batches.
- Disable the `discard current image` button on canvas if there is only one image. Prevents accidentally canceling all canvas batches if you spam the button.
Changed fields to go in w/h x/y order.
## Summary
The prior ordering of height, then width, and y, then x, doesn't match
up with the expected UX. This has been changed.
## Checklist
- [X] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
This is intended for debug usage, so it's hidden away in the workflow library `...` menu. Hold shift to see the button for it.
- Paste a graph (from a network request, for example) and then click the convert button to convert it to a workflow.
- Disable auto layout to stack the nodes with an offset (try it out). If you change this, you must re-convert to get the changes.
- Edit the workflow JSON if you need to tweak something before loading it.
- Allow user-defined precision on MPS.
- Use more explicit logic to handle all possible cases.
- Add comments.
- Remove the app_config args (they were effectively unused, just get the config using the singleton getter util)
This data is already in the template but it wasn't ever used.
One big place where this improves UX is the noise node. Previously, the UI let you change width and height in increments of 1, despite the template requiring a multiple of 8. It now works in multiples of 8.
Retrieving the DTO happens as part of the metadata parsing, not recall. This way, we don't show the option to recall a nonexistent image.
This matches the flow for other metadata entities like models - we don't show the model recall button if the model isn't available.
The previous algorithm errored if the image wasn't divisible by the tile size. I've reimplemented it from scratch to mitigate this issue.
The new algorithm is simpler. We create a pool of tiles, then use them to create an image composed completely of tiles. If there is any awkwardly sized space on the edge of the image, the tiles are cropped to fit.
Finally, paste the original image over the tile image.
I've added a jupyter notebook to do a smoke test of infilling methods, and 10 test images.
The other infill algorithms can be easily tested with the notebook on the same images, though I didn't set that up yet.
Tested and confirmed this gives results just as good as the earlier infill, though of course they aren't the same due to the change in the algorithm.
We have had a few bugs with v4 related to file encodings, especially on Windows.
Windows uses its own character encodings instead of `utf-8`, often `cp1252`. Some characters cannot be decoded using `utf-8`, causing `UnicodeDecodeError`.
There are a couple places where this can cause problems:
- In the installer bootstrap, we install or upgrade `pip` and decode the result, using `subprocess`.
The input to this includes the user's home dir. In #6105, the user had one of the problematic characters in their username. `subprocess` attempts and fails to decode the username, which crashes the installer.
To fix this, we need to use `locale.getpreferredencoding()` when executing the command.
- Similarly, in the model install service and config class, we attempt to load a yaml config file. If a problematic character is in the path to the file (which often includes the user's home dir), we can get the same error.
One example is #6129 in which the models.yaml migration fails.
To fix this, we need to open the file with `locale.getpreferredencoding()`.
- Remove `CUDA_AND_DML`. This was for onnx, which we have since removed.
- Remove `AUTODETECT`. This option causes problems for windows users, as it falls back on default pypi index resulting in a non-CUDA torch being installed.
- Add more explicit settings for extra index URL, based on the torch website
- Fix bug where `xformers` wasn't installed on linux and/or windows when autodetect was selected
This will be fairly common in v4 updates. The root cause is models not being added to the `models.yaml` file in v3, so we don't correctly migrate the models to the db.
The docs describe how to use `Scan Folder` to restore missing models.
Compare the installed paths to determine if the model is already installed. Fixes an issue where installed models showed up as uninstalled or vice-versa. Related to relative vs absolute path handling.
Renaming the model file to the model name introduces unnecessary contraints on model names.
For example, a model name can technically be any length, but a model _filename_ cannot be too long.
There are also constraints on valid characters for filenames which shouldn't be applied to model record names.
I believe the old behaviour is a holdover from the old system.
## Summary
This PR adds support for IP Adapter safetensor files for direct usage
inside InvokeAI.
# TEST
You can download the [Composition
Adapters](https://huggingface.co/ostris/ip-composition-adapter) which
weren't previously supported in Invoke and try them out. Every other IP
Adapter model should work too.
If you pick a Safetensor IP Adapter model, you will also need to set
ViT-H or ViT-G next to it. This is a raw implementation. Can refine it
further based on feedback.
Prompt: `Spiderman holding a bunny` -- Exact same composition as the
adapter image.
Setting to 'auto' works only for InvokeAI config and auto detects the SD model but will override if user explicitly sets it. If auto used with checkpoint models, we raise an error. Checkpoints will always need to set to non-auto.
The valid values for this parameter changed when inpainting changed to gradient denoise. The generation slice's redux migration wasn't updated, resulting in a generation error until you change the setting or reset web UI.
- Add and use more performant `deepClone` method for deep copying throughout the UI.
Benchmarks indicate the Really Fast Deep Clone library (`rfdc`) is the best all-around way to deep-clone large objects.
This is particularly relevant in canvas. When drawing or otherwise manipulating canvas objects, we need to do a lot of deep cloning of the canvas layer state objects.
Previously, we were using lodash's `cloneDeep`.
I did some fairly realistic benchmarks with a handful of deep-cloning algorithms/libraries (including the native `structuredClone`). I used a snapshot of the canvas state as the data to be copied:
On Chromium, `rfdc` is by far the fastest, over an order of magnitude faster than `cloneDeep`.
On FF, `fastest-json-copy` and `recursiveDeepCopy` are even faster, but are rather limited in data types. `rfdc`, while only half as fast as the former 2, is still nearly an order of magnitude faster than `cloneDeep`.
On Safari, `structuredClone` is the fastest, about 2x as fast as `cloneDeep`. `rfdc` is only 30% faster than `cloneDeep`.
`rfdc`'s peak memory usage is about 10% more than `cloneDeep` on Chrome. I couldn't get memory measurements from FF and Safari, but let's just assume the memory usage is similar relative to the other algos.
Overall, `rfdc` is the best choice for a single algo for all browsers. It's definitely the best for Chromium, by far the most popular desktop browser and thus our primary target.
A future enhancement might be to detect the browser and use that to determine which algorithm to use.
There were two ways the canvas history could grow too large (past the `MAX_HISTORY` setting):
- Sometimes, when pushing to history, we didn't `shift` an item out when we exceeded the max history size.
- If the max history size was exceeded by more than one item, we still only `shift`, which removes one item.
These issue could appear after an extended canvas session, resulting in a memory leak and recurring major GCs/browser performance issues.
To fix these issues, a helper function is added for both past and future layer states, which uses slicing to ensure history never grows too large.
Previously, exceptions raised as custom nodes are initialized were fatal errors, causing the app to exit.
With this change, any error on import is caught and the error message printed. App continues to start up without the node.
For example, a custom node that isn't updated for v4.0.0 may raise an error on import if it is attempting to import things that no longer exist.
Add `dump_path` arg to the converter function & save the model to disk inside the conversion function. This is the same pattern as in the other conversion functions.
Prefer an early return/continue to reduce the indentation of the processor loop. Easier to read.
There are other ways to improve its structure but at first glance, they seem to involve changing the logic in scarier ways.
This must not have been tested after the processors were unified. Needed to shift the logic around so the resume event is handled correctly. Clear and easy fix.
* pass model config to _load_model
* make conversion work again
* do not write diffusers to disk when convert_cache set to 0
* adding same model to cache twice is a no-op, not an assertion error
* fix issues identified by psychedelicious during pr review
* following conversion, avoid redundant read of cached submodels
* fix error introduced while merging
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
We switched all model paths to be absolute in #5900. In hindsight, this is a mistake, because it makes the `models_dir` non-portable.
This change reverts to the previous model pathing:
- Invoke-managed models (in the `models_dir`) are stored with relative paths
- Non-invoke-managed models (outside the `models_dir`, i.e. in-place installed models) still have absolute paths.
## Why absolute paths make things non-portable
Let's say my `models_dir` is `/media/rhino/invokeai/models/`. In the DB, all model paths will be absolute children of this path, like this:
- `/media/rhino/invokeai/models/sd-1/main/model1.ckpt`
I want to change my `models_dir` to `/home/bat/invokeai/models/`. I update my `invokeai.yaml` file and physically move the files to that directory.
On startup, the app checks for missing models. Because all of my model paths were absolute, they now point to a nonexistent path. All models are broken.
There are a couple options to recover from this situation, neither of which are reasonable:
1. The user must manually update every model's path. Unacceptable UX.
2. On startup, we check for missing models. For each missing model, we compare its path with the last-known models dir. If there is a match, we replace that portion of the path with the new models dir. Then we re-check to see if the path exists. If it does, we update the models DB entry. Brittle and requires a new DB entry for last-known models dir.
It's better to use relative paths for Invoke-managed models.
Setting to 'auto' works only for InvokeAI config and auto detects the SD model but will override if user explicitly sets it. If auto used with checkpoint models, we raise an error. Checkpoints will always need to set to non-auto.
The seamless logic errors when a second GPU is selected. I don't understand why, but a workaround is to skip the model patching when there there are no seamless axes specified.
This is also just a good practice regardless - don't patch the model unless we need to. Probably a negligible perf impact.
Closes#6010
The build workflow was naming the file `InvokeAI-installer-v4.0.0rc6.zip.zip` (note the double ".zip"). This caused some confusion when creating releases on GitHub.
Name the build artifact `installer`. This results in `installer.zip`, which it's clear needs to be extracted first before uploading to the GH release.
`scripts/get_external_contributions.py` gets all commits between two refs and outputs a summary.
Useful for getting all external contributions for release notes.
There's still a few references in `WEB.md` but this doc is very outdated and needs to be totally redone. It's hard to just remove the references without redoing a lot more.
Will need to follow up revising this doc.
These two changes are interrelated.
## Autoimport
The autoimport feature can be easily replicated using the scan folder tab in the model manager. Removing the implicit autoimport reduces surface area and unifies all model installation into the UI.
This functionality is removed, and the `autoimport_dir` config setting is removed.
## Startup model dir scanning
We scanned the invoke-managed models dir on startup and took certain actions:
- Register orphaned model files
- Remove model records from the db when the model path doesn't exist
### Orphaned model files
We should never have orphaned model files during normal use - we manage the models directory, and we only delete files when the user requests it.
During testing or development, when a fresh DB or memory DB is used, we could end up with orphaned models that should be registered.
Instead of always scanning for orphaned models and registering them, we now only do the scan if the new `scan_models_on_startup` config flag is set.
The description for this setting indicates it is intended for use for testing only.
### Remove records for missing model files
This functionality could unexpectedly wipe models from the db.
For example, if your models dir was on external media, and that media was inaccessible during startup, the scan would see all your models as missing and delete them from the db.
The "proactive" scan is removed. Instead, we will scan for missing models and log a warning if we find a model whose path doesn't exist. No possibility for data loss.
I had added this because I mistakenly believed the HF token was required to download HF models.
Turns out this is not the case, and the vast majority of HF models do not need the API token to download.
"Normal" models have 4 in-channels, while "Depth" models have 5 and "Inpaint" models have 9.
We need to explicitly tell diffusers the channel count when converting models.
Closes #6058
It's possible for a model's state dict to have integer keys, though we do not actually support such models.
As part of probing, we call `key.startswith(...)` on the state dict keys. This raises an `AttributeError` for integer keys.
This logic is in `invokeai/backend/model_manager/probe.py:get_model_type_from_checkpoint`
To fix this, we can cast the keys to strings first. The models w/ integer keys will still fail to be probed, but we'll get a `InvalidModelConfigException` instead of `AttributeError`.
Closes#6044
Previously we only handled expected error types. If a different error was raised, the install job would end up in an unexpected state where it has failed and isn't doing anything, but its status is still running.
This indirectly prevents the installer threads from exiting - they are waiting for all jobs to be completed, including the failed-but-still-running job.
We need to handle any error here to prevent this.
This allows us to easily test the installer without needing the desired version to be published on PyPI:
```sh
python3 installer/lib/main.py --wheel installer/dist/InvokeAI-4.0.0rc6-py3-none-any.whl
```
A warning message and confirmation are displayed when the arg is used.
The rest of the installer is unchanged.
Updating should always be done via the installer. We initially planned to only deprecate the updater, but given the scale of changes for v4, there's no point in waiting to remove it entirely.
Loading default workflows sometimes requires we mutate the workflow object in order to change the category or ID of the workflow.
This happens in `invokeai/frontend/web/src/features/nodes/util/workflow/validateWorkflow.ts`
The data we get back from the query hooks is frozen and sealed by redux, because they are part of redux state. We need to clone the workflow before operating on it.
It's not clear how this ever worked in the past, because redux state has always been frozen and sealed.
Add `extra="forbid"` to the default settings models.
Closes#6035.
Pydantic has some quirks related to unions. This affected how the union of default settings was evaluated. See https://github.com/pydantic/pydantic/issues/9095 for a detailed description of the behaviour that this change addresses.
- Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own.
- Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
* Fix minor bugs involving model manager handling of model paths
- Leave models found in the `autoimport` directory there. Do not move them
into the `models` hierarchy.
- If model name, type or base is updated and model is in the `models` directory,
update its path as appropriate.
- On startup during model scanning, if a model's path is a symbolic link, then resolve
to an absolute path before deciding it is a new model that must be hashed and
registered. (This prevents needless hashing at startup time).
* fix issue with dropped suffix
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Currently translated at 98.2% (1102 of 1122 strings)
translationBot(ui): update translation (Italian)
Currently translated at 97.9% (1099 of 1122 strings)
translationBot(ui): update translation (Italian)
Currently translated at 97.9% (1099 of 1122 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
- Add patched rootdir fixture to all config tests. I think this isn't strictly necessary but it does ensure that any config tests that need to write files don't accidentally write to user data locations.
- Be more careful when calling `get_config()` in the tests, by clearing the LRU cache before and after. This ensures a test doesn't reference the singleton config created by a previously run test.
- Add test for env var parsing.
- Add test for config writing in the context of `get_config()`. This is effectively a mini e2e test for the config lifecycle.
Add class `DefaultInvokeAIAppConfig`, which inherits from `InvokeAIAppConfig`. When instantiated, this class does not parse environment variables, so it outputs a "clean" default config. That's the only difference.
Then, we can use this new class in the 3 places:
- When creating the example config file (no env vars should be here)
- When migrating a v3 config (we want to instantiate the migrated config without env vars, so that when we write it out, they are not written to disk)
- When creating a fresh config file (i.e. on first run with an uninitialized root or new config file path - no env vars here!)
For SSDs, `blake3` is about 10x faster than `blake3_single` - 3 files/second vs 30 files/second.
For spinning HDDs, `blake3` is about 100x slower than `blake3_single` - 300 seconds/file vs 3 seconds/file.
For external drives, `blake3` is always worse, but the difference is highly variable. For external spinning drives, it's probably way worse than internal.
The least offensive algorithm is `blake3_single`, and it's still _much_ faster than any other algorithm.
With the change to model identifiers from v3 to v4, if a user had persisted redux state with the old format, we could get unexpected runtime errors when rehydrating state if we try to access model attributes that no longer exist.
For example, the CLIP Skip component does this:
```ts
CLIP_SKIP_MAP[model.base].maxClip
```
In v3, models had a `base_type` attribute, but it is renamed to `base` in v4. This code therefore causes a runtime error:
- `model.base` is `undefined`
- `CLIP_SKIP_MAP[undefined]` is also undefined
- `undefined.maxClip` is a runtime error!
Resolved by adding a migration for the redux slices that have model identifiers. The migration simply resets the slice or the part of the slice that is affected, when it's simple to do a partial reset.
Closes#6000
If you switch between different branches, by the time you get back to `main`, a different version of `ruff` might be installed that has slightly different formatting rules. This leads to incorrect formatting changes.
Pinning `ruff` avoids this issue.
* add probe for SDXL controlnet models
* Update invokeai/backend/model_management/model_probe.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Update invokeai/backend/model_manager/probe.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
These all support controlnet processors.
- `pil_to_cv2`
- `cv2_to_pil`
- `pil_to_np`
- `np_to_pil`
- `normalize_image_channel_count` (a readable version of `HWC3` from the controlnet repo)
- `fit_image_to_resolution` (a readable version of `resize_image` from the controlnet repo)
- `non_maximum_suppression` (a readable version of `nms` from the controlnet repo)
- `safe_step` (a readable version of `safe_step` from the controlnet repo)
Some processors, like Canny, didn't use `detect_resolution`. The resultant control images were then resized by the processors from 512x512 to the desired dimensions. The result is that the control images are the right size, but very low quality.
Using detect_resolution fixes this.
- Display a toast on UI launch if the HF token is invalid
- Show form in MM if token is invalid or unable to be verified, let user set the token via this form
This allows users to create simple "profiles" via separate `invokeai.yaml` files.
- Remove `InvokeAIAppConfig.set_root()`, it's extraneous
- Remove `InvokeAIAppConfig.merge_from_file()`, it's extraneous
- Add `--config` to the app arg parser, add `InvokeAIAppConfig._config_file`, and consume in the config singleton getter
- `InvokeAIAppConfig.init_file_path` -> `InvokeAIAppConfig.config_file_path`
The models from INITIAL_MODELS.yaml have been recreated as a structured python object. This data is served on a new route. The model sources are compared against currently-installed models to determine if they are already installed or not.
This flag acts as a proxy for the `get_config()` function to determine if the full application is running.
If it was, the config will set the root, do HF login, etc.
If not (e.g. it's called by an external script), all that stuff will be skipped.
HF login, legacy yaml confs, and default init file are all handled during app setup.
All directories are created as they are needed by the app.
No need to check for a valid root dir - we will make it if it doesn't exist.
This provides a simple way to provide a HF token. If HF reports no valid token, one is prompted for until a valid token is provided, or the user presses Ctrl + C to cancel.
This simple package provides a cross-platform way to type a password on the CLI and have it show up as asterisks.
The fork, pending merge into the upstream package, adds support for Ctrl+C to cancel input.
Use the util function to calculate ram cache size on startup. This way, the `ram` setting will always be optimized for a system, even if they add or remove RAM. In other words, the default value is now dynamic.
- Move base of t2i and clip_vision config models to DiffusersBase, which contains
a field to record the model variant (e.g. "fp16")
- This restore the ability to load fp16 t2i and clip_vision models
- Also add defensive coding to load the vanilla model when the fp16 model
has been replaced (or more likely, user's preferences changed since installation)
Co-authored-by: Lincoln Stein <lstein@gmail.com>
When consolidating all the model queries I messed up the query tags. Fixed now, so that when a model is installed, removed, or changed, the list refreshes.
Currently translated at 52.5% (576 of 1096 strings)
translationBot(ui): update translation (Japanese)
Currently translated at 52.0% (570 of 1096 strings)
Co-authored-by: Gohsuke Shimada <ghoskay@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ja/
Translation: InvokeAI/Web UI
Currently translated at 97.8% (1510 of 1543 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.1% (1503 of 1532 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.1% (1503 of 1532 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
In order to allow for null and undefined metadata values, this hook returned a symbol to indicate that parsing failed or was pending.
For values where the parsed value will never be null or undefined, it is useful get the value or null (instead of a symbol).
When running the configurator, the `legacy_models_conf_path` was stripped when saving the config file. Then the migration logic didn't fire correctly, and the custom models.yaml paths weren't migrated into the db.
- Rework the logic to migrate this path by adding it to the config object as a normal field that is not excluded from serialization.
- Rearrange the models.yaml migration logic to remove the legacy path after migrating, then write the config file. This way, the legacy path doesn't stick around.
- Move the schema version into the config object.
- Back up the config file before attempting migration.
- Add tests to cover this edge case
Hold onto `conf_path` temporarily while migrating `invokeai.yaml` so that it gets migrated correctly as the model installer starts up. Stashed as `legacy_models_yaml_path` in the config, excluded from serialization.
We have two problems with how argparse is being utilized:
- We parse CLI args as the `api_app.py` file is read. This causes a problem pytest, which has an incompatible set of CLI args. Some tests import the FastAPI app, which triggers the config to parse CLI args, which receives the pytest args and fails.
- We've repeatedly had problems when something that uses the config is imported before the CLI args are parsed. When this happens, the root dir may not be set correctly, so we attempt to operate on incorrect paths.
To resolve these issues, we need to lift CLI arg parsing outside of the application code, but still let the application access the CLI args. We can create a external app entrypoint to do this.
- `InvokeAIArgs` is a simple helper class that parses CLI args and stores the result.
- `run_app()` is the new entrypoint. It first parses CLI args, then runs `invoke_api` to start the app.
The `invokeai-web` project script and `invokeai-web.py` dev script now call `run_app()` instead of `invoke_api()`.
The first time `get_config()` is called to get the singleton config object, it retrieves the args from `InvokeAIArgs`, sets the root dir if provided, then merges settings in from `invokeai.yaml`.
CLI arg parsing is now safely insulated from application code, but still accessible. And we don't need to worry about import order having an impact on anything, because by the time the app is running, we have already parsed CLI args. Whew!
This fixes an issue with `test_images.py`, which tests the bulk images routers and imports the whole FastAPI app. This triggers the config logic which fails on the test runner, because it has no `invokeai.yaml`.
Also probably just good for graceful fallback.
- `write_file` requires an destination file path
- `read_config` -> `merge_from_file`, if no path is provided, reads from `self.init_file_path`
- update app, tests to use new methods
- fix configurator, was overwriting config file data unexpectedly
Tweak the name of it so that incoming configs with the old default value of 6 have the setting stripped out. The result is all configs will now have the new, much better default value of 1.
Having this all in the `get_config` function makes testing hard. Move these two functions to their own methods, and call them on app startup explicitly.
- Remove OmegaConf. It functioned as an intermediary data format, between YAML/argparse and pydantic. It's not necessary - we can parse YAML or CLI args directly with pydantic.
- Remove dynamic CLI args. Only `root` is explicitly supported. This greatly simplifies config handling. Configuration is done by editing the YAML file. Frequently-used args can be added if there is a demand.
- A separate arg parser is created to handle the slimmed-down CLI args. It's run immediately in the `invokeai-web` script to handle `--version` and `--help`. It is also used inside the singleton config getter (see below).
- Remove categories from the config. Our settings model is mostly flat. Handling categories adds complexity for both us and users - we have to handle transforming a flat config to categorized config (and vice-versa), while users have to be careful with indentation in their YAML file.
- Add a `meta` key to the config file. Currently, this holds the config schema version only. It is not a part of the config object itself.
- Remove legacy settings that are no longer referenced, or were effectively no-op settings when referenced in code.
- Implement simple migration logic to for v3 configs. If migration is successful, the v3 config file is backed up to `invokeai.yaml.bak` and the new config written to `invokeai.yaml`.
- Previously, the singleton config was accessed by calling `InvokeAIAppConfig.get_config()`. This returned an instance of `InvokeAIAppConfig`, which _also_ has the `get_config` function. This created to a confusing situation where you weren't sure if you needed to call `get_config` or just use the config object. This method is replaced by a standalone `get_config` function which returns a singleton config object.
- Wrap CLI arg parsing (for `root`) and loading/migrating `invokeai.yaml` into the new `get_config()` function.
- Move `generate_config_docstrings` into standalone utility function.
- Make `root` a private attr (`_root`). This reduces the temptation to directly modify and or use this sensitive field and ensures it is neither serialized nor read from input data. Use `root_path` to access the resolved root path, or `set_root` to set the root to something.
* allow removal of models with legacy relative path addressing
* added five controlnet models for sdxl to INITIAL_MODELS
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
We've been using a forked copy of the diffusers safetensors->diffusers
model conversion code, which was hacked to read CLIP and the other
models needed for conversion from the local invokeai root models
directory. This was getting unsustainable as the code bases diverged,
and also required the installation and maintenance of the "core/convert"
directory.
This PR gets rid of the hacked conversion code and reverts to using the
native diffusers methods. Core convert models are no longer installed at
root configure time. Instead we rely on the HuggingFace hub system to
download the conversion models if and when they are needed. They are
relatively small and the initial delay seems minor.
Conversion of SD-1, SD-2 (both epsilon and v-prediction), SDXL, VAE and
ControlNet SD-1/2 models has been tested. ControlNet SDXL models are
still a WIP due to the need for some work on the prober.
The main implication of this change is that InvokeAI is no longer
internet-independent and will need an internet connection at least the
first time a safetensors file needs to be converted. However, there are
several other places where the "no internet" rule is already violated,
and I suggest that we abandon this principle.
## Related Tickets & Documents
<!--
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- Related Issue #
- Closes#5964
## QA Instructions, Screenshots, Recordings
1. Remove or move `$INVOKEAI_ROOT/models/.cache`
2. Move `$INVOKEAI/models/core/convert`
3. Try generating with an unconverted .safetensors model.
<!--
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software specifications as well as any other pertinent information.
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## Merge Plan
Merge when approved.
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
- No longer install core conversion models. Use the HuggingFace cache to load
them if and when needed.
- Call directly into the diffusers library to perform conversions with only shallow
wrappers around them to massage arguments, etc.
- At root configuration time, do not create all the possible model subdirectories,
but let them be created and populated at model install time.
- Remove checks for missing core conversion files, since they are no
longer installed.
In the client, a controlnet or t2i adapter has two images:
- The source control image: the image the user selected (required)
- The processed control image: the user's image after we've processed it (optional)
The processed image is optional because a user may provide a pre-processed image.
We only actually use one of these images when building the graph, and until this change, we only stored one of the in image metadata. This created a situation where only a processed image was stored in metadata - say, a canny edge map - and the user-selected image wasn't provided.
By adding the processed image to metadata, we can recall both the control image and optional processed image.
This commit is followed by a UI-facing changes to support the change.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [z] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Single query, with simple wrapper hooks (type-safe). Updated everywhere
in frontend.
## QA Instructions, Screenshots, Recordings
Things that use models should work. All of this code is strictly
typechecked, so we can be confident in this change.
<!--
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software specifications as well as any other pertinent information.
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## Merge Plan
This PR can be merged when approved
<!--
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- "DO NOT MERGE - I will rebase and tidy commits before merging"
- "#dev-chat on discord needs to be advised of this change when it is
merged"
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the
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We were passing a PIL image when we needed to pass the np image.
Closes#5956
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
We were passing a PIL image when we needed to pass the np image.
Closes#5956
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes#5956
## QA Instructions, Screenshots, Recordings
Depth anything processor should work.
<!--
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software specifications as well as any other pertinent information.
-->
## Merge Plan
This PR can be merged when approved
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merged"
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- This adds additional logic to the safetensors->diffusers conversion script
to check for and install missing core conversion models at runtime.
- Fixes#5934
BLAKE3 has poor performance on spinning disks when parallelized. See https://github.com/BLAKE3-team/BLAKE3/issues/31
- Replace `skip_model_hash` setting with `hashing_algorithm`. Any algorithm we support is accepted.
- Add `random` algorithm: hashes a UUID with BLAKE3 to create a random "hash". Equivalent to the previous skip functionality.
- Add `blake3_single` algorithm: hashes on a single thread using BLAKE3, fixes the aforementioned performance issue
- Update model probe to accept the algorithm to hash with as an optional arg, defaulting to `blake3`
- Update all calls of the probe to use the app's configured hashing algorithm
- Update an external script that probes models
- Update tests
- Move ModelHash into its own module to avoid circuclar import issues
This script removes unused translations from the `en.json` source translation file:
- Parse `en.json` to build a list of all keys, e.g. `controlnet.depthAnything`
- Check every frontend file for every key
- If the key is not found, it is removed from the translation file
- Exact matches (e.g. `controlnet.depthAnything`) and stem matches (e.g. `depthAnything`) are ignored
The graph builders used awaited functions within `Array.prototype.forEach` loops. This doesn't do what you'd think. This caused graphs to be enqueued before they were fully constructed.
Changed to `for..of` loops to fix this.
There wasn't enough validation of control adapters during graph building. It would be possible for a graph to be built with empty collect node, causing an error. Addressed with an extra check.
This should never happen in practice, because the invoke button should be disabled if an invalid CA is active.
## What type of PR is this? (check all applicable)
- [x] Optimization
## Description
Was merged into next but never carried over to main. So cleaning up
again.
This bypasses the `changed-files` check, and forces the checks to run. The release workflow sets this flag to ensure that the checks and tests are always run for a release.
…ention processors if no mid_block is detected
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
L2i throws an assertion error when run with `madebyollin/taesdxl` due to
it requiring a different class in diffusers to load it. This is a small
PR to update seamless and l2i to accept AutoencoderTiny models and not
throw exceptions while processing them.
## QA Instructions, Screenshots, Recordings
<img width="445" alt="Screenshot 2024-03-12 at 12 04 29 PM"
src="https://github.com/invoke-ai/InvokeAI/assets/58442074/34a17e44-d911-4fef-8fc1-71f7b688688c">
Run an sdxl pipeline using a vae that requires AutoencoderTiny and
validate that the image successfully encodes and decodes.
## Merge Plan
This PR can be merged when approved
We were stripping the file extension from file models when moving them in `_sync_model_path`. For example, `some_model.safetensors` would be moved to `some_model`, which of course breaks things.
Instead of using the model's name as the new path, use the model's path's last segment. This is the same behaviour for directories, but for files, it retains the file extension.
- No need for it to by a pydantic model. Just a class now.
- Remove ABC, it made it hard to understand what was going on as attributes were spread across the ABC and implementation. Also, there is no other implementation.
- Add tests
- If the metadata yaml has an invalid version, exist the app. If we don't, the app will crawl the models dir and add models to the db without having first parsed `models.yaml`. This should not happen often, as the vast majority of users are on v3.0.0 models.yaml files.
- Fix off-by-one error with models count (need to pop the `__metadata__` stanza
- After a successful migration, rename `models.yaml` to `models.yaml.bak` to prevent the migration logic from re-running on subsequent app startups.
The old logic to check if a model needed to be moved relied on the model path being a relative path. Paths are now absolute, causing this check to fail. We then assumed the paths were different and moved the model from its current location to, well, its current location.
Use more resilient method to check if a model should be moved.
mkdocs can autogenerate python class docs from its docstrings. Our config is a pydantic model.
It's tedious and error-prone to duplicate docstrings from the pydantic field descriptions to the class docstrings.
- Add helper function to generate a mkdocs-compatible docstring from the InvokeAIAppConfig class fields
Recently the schema for models was changed to a generic `ModelField`, and the UI was unable to derive the type of those fields. This didn't affect functionality, but it did break the styling of handles.
Add `ui_type` to the affected fields and update the UI to use the correct capitalizations.
A list of regex and token pairs is accepted. As a file is downloaded by the model installer, the URL is tested against the provided regex/token pairs. The token for the first matching regex is used during download, added as a bearer token.
Without this, the form will incorrectly compare its state to its initial default values to determine if it is dirty. Instead, it should reset its default values to the new values after successful submit.
When we change a model image, its URL remains the same. The browser will aggressively cache the image. The easiest way to fix this is to append a random query parameter to the URL whenever we build a model config in the API.
- Move image display to left
- Move description into model header
- Move model edit & convert buttons to top right of model header
- Tweak styles for model display component
Currently translated at 98.0% (1487 of 1516 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.0% (1482 of 1512 strings)
translationBot(ui): update translation (Italian)
Currently translated at 98.0% (1475 of 1505 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
- All models are identified by a key and optionally a submodel type via new model `ModelField`. Previously, a few model types had their own class, but not all of them. This inconsistency just added complexity without any benefit.
- Update all invocation to use the new format.
- In the node API, models are loaded by key or an instance of `ModelField` as a convenience.
- Add an enriched model schema for metadata. It includes key, hash, name, base and type.
In order for delete by match to work, we need the whole invocation output to be stringified.
For some reason, the serialization of the output was set to only include the `type` field. It should instead include the whole output.
I don't understand how this ever worked unless pydantic had different serialization behaviour in v1 (though it appears to have been the same).
Closes#5805
* move defaultModel logic to modelsLoaded and update to work for key instead of name/base/type string
* lint fix
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
- Update all queries
- Remove Advanced Add
- Removed un-editable, internal-only model attributes from model edit UI (e.g. format, repo variant, model type)
- Update model tags so the list refreshes when a model installs
- Rename some queries, components, variables, types to match backend
- Fix divide-by-zero in install queue
Rename MM routes to be consistent:
- "import" -> "install"
- "model_record" -> "model"
Comment several unused routes while I work (may end up removing them?):
- list model summary (we use the search route instead)
- add model record
- convert model
- merge models
There is a breaking change in python 3.11 related to how enums with `str` as a mixin are formatted. This appears to have not caused any grief for us until now.
Re-jigger the discriminator setup to use `.value` so everything works on both python 3.10 and 3.11.
- Metadata is merged with the config. We can simplify the MM substantially and remove the handling for metadata.
- Per discussion, we don't have an ETA for frontend implementation of tags, and with the realization that the tags from CivitAI are largely useless, there's no reason to keep tags in the MM right now. When we are ready to implement tags on the frontend, we can refer back to the implementation here and use it if it supports the design.
- Fix all tests.
Sometimes, diffusers model components (tokenizer, unet, etc.) have multiple weights files in the same directory.
In this situation, we assume the files are different versions of the same weights. For example, we may have multiple
formats (`.bin`, `.safetensors`) with different precisions. When downloading model files, we want to select only
the best of these files for the requested format and precision/variant.
The previous logic assumed that each model weights file would have the same base filename, but this assumption was
not always true. The logic is revised score each file and choose the best scoring file, resulting in only a single
file being downloaded for each submodel/subdirectory.
* UI in MM to create trigger phrases
* add scheduler and vaePrecision to config
* UI for configuring default settings for models'
* hook MM default model settings up to API
* add button to set default settings in parameters
* pull out trigger phrases
* back-end for default settings
* lint
* remove log;
gi
* ruff
* ruff format
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
- Use memory view for hashlib algorithms (closer to python 3.11's filehash API in hashlib)
- Remove `sha1_fast` (realized it doesn't even hash the whole file, it just does the first block)
- Add support for custom file filters
- Update docstrings
- Update tests
- When installing, model keys are now calculated from the model contents.
- .safetensors, .ckpt and other single file models are hashed with sha1
- The contents of diffusers directories are hashed using imohash (faster)
fixup yaml->sql db migration script to assign deterministic key
- this commit also detects and assigns the correct image encoder for
ip adapter models.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because
## Description
Attention map saving was a feature that existed a long time ago in
Invoke (>1 year ago). This PR strips out a bunch of dead code that still
remains from that feature and is polluting our diffusion implementation.
This change should not have any functional effect on the app.
## QA Instructions, Screenshots, Recordings
I did a quick smoke test of SD and SDXL image generation. All of the
deleted code was unused, so the risk should be relatively low.
## Merge Plan
- [x] Change target branch to `main` before merging.
## Added/updated tests?
- [ ] Yes
- [x] No: This PR just deletes a bunch of unused code.
The timeouts are at least 3x the expected time to complete the jobs.
This is particularly relevant for the `pytest` job. Occasionally, it hangs while running tests that do network things, and the job only times out after 6 hours.
- Restructure & update code check workflows
- Add release workflow to handle checks/tests, build and publish to PyPI
- Add docs/RELEASE.md explaining the workflow & process
- `create_installer.sh`: Update to work with the release workflow
- `create_installer.sh` & `tag_release.sh`: Fix the ANSI escape codes for macOS
- `tag_release.sh`: Add check for python binary name
- `tag_release.sh`: Print `git remote -v` output
- `tag_release.sh`: Fix error when deleting nonexistant tags
This ensures it matches the github workflow.
Also there's an update that stabilizes a number of formatting rules, so there will be a format commit after this.
Model metadata includes the main model, VAE and refiner model.
These used full model configs, as returned by the server, as their metadata type.
LoRA and control adapter metadata only use the metadata identifier.
This created a difference in handling. After parsing a model/vae/refiner, we have its name and can display it. But for LoRAs and control adapters, we only have the model key and must query for the full model config to get the name.
This change makes main model/vae/refiner metadata only have the model key, like LoRAs and control adapters.
The render function is now async so fetching can occur within it. All metadata fields with models now only contain the identifier, and fetch the model name to render their values.
When we retrieve a list of models, upsert that data into the `getModelConfig` and `getModelConfigByAttrs` query caches.
With this change, calls to those two queries are almost always going to be free, because their caches will already have all models in them. The exception is queries for models that no longer exist.
Add concepts for metadata handlers. Handlers include parsers, recallers and validators for different metadata types:
- Parsers parse a raw metadata object of any shape to a structured object.
- Recallers load the parsed metadata into state. Recallers are optional, as some metadata types don't need to be loaded into state.
- Validators provide an additional layer of validation before recalling the metadata. This is needed because a metadata object may be valid, but not able to be recalled due to some other requirement, like base model compatibility. Validators are optional.
Sometimes metadata is not a single object but a list of items - like LoRAs. Metadata handlers may implement an optional set of "item" handlers which operate on individual items in the list.
Parsers and validators are async to allow fetching additional data, like a model config. Recallers are synchronous.
The these handlers are composed into a public API, exported as a `handlers` object. Besides the handlers functions, a metadata handler set includes:
- A function to get the label of the metadata type.
- An optional function to render the value of the metadata type.
- An optional function to render the _item_ value of the metadata type.
Gets the first model that matches the given name, base and type. Raises 404 if there isn't one.
This will be used for backwards compatibility with old metadata.
This was done in the frontend before but it's something the backend should handle.
The logic compares the found model paths to the path and source of all installed models. It excludes core models.
Refactor of metadata recall handling. This is in preparation for a backwards compatibility layer for models.
- Create helpers to fetch a model outside react (e.g. not in a hook)
- Created helpers to parse model metadata
- Renamed a lot of types that were confusing and/or had naming collisions
The setup of `ModelConfigBase` means autogenerated types have critical fields flagged as nullable (like `key` and `base`). Need to manually flag them as required.
- Support extended HF repoid syntax in TUI. This allows
installation of subfolders and safetensors files, as in
`XpucT/Deliberate::Deliberate_v5.safetensors`
- Add `error` and `error_traceback` properties to the install
job objects.
- Rename the `heuristic_import` route to `heuristic_install`.
- Fix the example `config` input in the `heuristic_install` route.
Notable updates:
- Minor version of RTK includes customizable selectors for RTK Query, so we can remove the patch that was added to ensure only the LRU memoize function was used for perf reasons. Updated to use the LRU memoize function.
- Major version of react-resizable-panels. No breaking changes, works great, and you can now resize all panels when dragging at the intersection point of panels. Cool!
- Minor (?) version of nanostores. `action` API is removed, we were using it in one spot. Fixed.
- @invoke-ai/eslint-config-react has all deps bumped and now has its dependent plugins/configs listed as normal dependencies (as opposed to peer deps). This means we can remove those packages from explicit dev deps.
- Use a single listener for all of the to keep them in one spot
- Use the bulk download item name as a toast id so we can update the existing toasts
- Update handling to work with other environments
- Move all bulk download handling from components to listener
- Deduplicate the mock invocation services. This is possible now that the import order issue is resolved.
- Merge `DummyEventService` into `TestEventService` and update all tests to use `TestEventService`.
Double underscores are used in the app but it doesn't actually do or convey anything that single underscores don't already do. Considered unpythonic except for actual dunder/magic methods.
Consolidate graph processing logic into session processor.
With graphs as the unit of work, and the session queue distributing graphs, we no longer need the invocation queue or processor.
Instead, the session processor dequeues the next session and processes it in a simple loop, greatly simplifying the app.
- Remove `graph_execution_manager` service.
- Remove `queue` (invocation queue) service.
- Remove `processor` (invocation processor) service.
- Remove queue-related logic from `Invoker`. It now only starts and stops the services, providing them with access to other services.
- Remove unused `invocation_retrieval_error` and `session_retrieval_error` events, these are no longer needed.
- Clean up stats service now that it is less coupled to the rest of the app.
- Refactor cancellation logic - cancellations now originate from session queue (i.e. HTTP cancel endpoint) and are emitted as events. Processor gets the events and sets the canceled event. Access to this event is provided to the invocation context for e.g. the step callback.
- Remove `sessions` router; it provided access to `graph_executions` but that no longer exists.
`GraphInvocation` is a node that can contain a whole graph. It is removed for a number of reasons:
1. This feature was unused (the UI doesn't support it) and there is no plan for it to be used.
The use-case it served is known in other node execution engines as "node groups" or "blocks" - a self-contained group of nodes, which has group inputs and outputs. This is a planned feature that will be handled client-side.
2. It adds substantial complexity to the graph processing logic. It's probably not enough to have a measurable performance impact but it does make it harder to work in the graph logic.
3. It allows for graphs to be recursive, and the improved invocations union handling does not play well with it. Actually, it works fine within `graph.py` but not in the tests for some reason. I do not understand why. There's probably a workaround, but I took this as encouragement to remove `GraphInvocation` from the app since we don't use it.
The change to `Graph.nodes` and `GraphExecutionState.results` validation requires some fanagling to get the OpenAPI schema generation to work. See new comments for a details.
We use pydantic to validate a union of valid invocations when instantiating a graph.
Previously, we constructed the union while creating the `Graph` class. This introduces a dependency on the order of imports.
For example, consider a setup where we have 3 invocations in the app:
- Python executes the module where `FirstInvocation` is defined, registering `FirstInvocation`.
- Python executes the module where `SecondInvocation` is defined, registering `SecondInvocation`.
- Python executes the module where `Graph` is defined. A union of invocations is created and used to define the `Graph.nodes` field. The union contains `FirstInvocation` and `SecondInvocation`.
- Python executes the module where `ThirdInvocation` is defined, registering `ThirdInvocation`.
- A graph is created that includes `ThirdInvocation`. Pydantic validates the graph using the union, which does not know about `ThirdInvocation`, raising a `ValidationError` about an unknown invocation type.
This scenario has been particularly problematic in tests, where we may create invocations dynamically. The test files have to be structured in such a way that the imports happen in the right order. It's a major pain.
This PR refactors the validation of graph nodes to resolve this issue:
- `BaseInvocation` gets a new method `get_typeadapter`. This builds a pydantic `TypeAdapter` for the union of all registered invocations, caching it after the first call.
- `Graph.nodes`'s type is widened to `dict[str, BaseInvocation]`. This actually is a nice bonus, because we get better type hints whenever we reference `some_graph.nodes`.
- A "plain" field validator takes over the validation logic for `Graph.nodes`. "Plain" validators totally override pydantic's own validation logic. The validator grabs the `TypeAdapter` from `BaseInvocation`, then validates each node with it. The validation is identical to the previous implementation - we get the same errors.
`BaseInvocationOutput` gets the same treatment.
- Replace AnyModelLoader with ModelLoaderRegistry
- Fix type check errors in multiple files
- Remove apparently unneeded `get_model_config_enum()` method from model manager
- Remove last vestiges of old model manager
- Updated tests and documentation
resolve conflict with seamless.py
- Rename old "model_management" directory to "model_management_OLD" in order to catch
dangling references to original model manager.
- Caught and fixed most dangling references (still checking)
- Rename lora, textual_inversion and model_patcher modules
- Introduce a RawModel base class to simplfy the Union returned by the
model loaders.
- Tidy up the model manager 2-related tests. Add useful fixtures, and
a finalizer to the queue and installer fixtures that will stop the
services and release threads.
- ModelMetadataStoreService is now injected into ModelRecordStoreService
(these two services are really joined at the hip, and should someday be merged)
- ModelRecordStoreService is now injected into ModelManagerService
- Reduced timeout value for the various installer and download wait*() methods
- Introduced a Mock modelmanager for testing
- Removed bare print() statement with _logger in the install helper backend.
- Removed unused code from model loader init file
- Made `locker` a private variable in the `LoadedModel` object.
- Fixed up model merge frontend (will be deprecated anyway!)
- Update most model identifiers to be `{key: string}` instead of name/base/type. Doesn't change the model select components yet.
- Update model _parameters_, stored in redux, to be `{key: string, base: BaseModel}` - we need to store the base model to be able to check model compatibility. May want to store the whole config? Not sure...
- Replace legacy model manager service with the v2 manager.
- Update invocations to use new load interface.
- Fixed many but not all type checking errors in the invocations. Most
were unrelated to model manager
- Updated routes. All the new routes live under the route tag
`model_manager_v2`. To avoid confusion with the old routes,
they have the URL prefix `/api/v2/models`. The old routes
have been de-registered.
- Added a pytest for the loader.
- Updated documentation in contributing/MODEL_MANAGER.md
- Implement new model loader and modify invocations and embeddings
- Finish implementation loaders for all models currently supported by
InvokeAI.
- Move lora, textual_inversion, and model patching support into
backend/embeddings.
- Restore support for model cache statistics collection (a little ugly,
needs work).
- Fixed up invocations that load and patch models.
- Move seamless and silencewarnings utils into better location
- Cache stat collection enabled.
- Implemented ONNX loading.
- Add ability to specify the repo version variant in installer CLI.
- If caller asks for a repo version that doesn't exist, will fall back
to empty version rather than raising an error.
Unfortunately you cannot test for both a specific type of error and match its message. Splitting the error classes makes it easier to test expected error conditions.
The changes aim to deduplicate data between workflows and node templates, decoupling workflows from internal implementation details. A good amount of data that was needlessly duplicated from the node template to the workflow is removed.
These changes substantially reduce the file size of workflows (and therefore the images with embedded workflows):
- Default T2I SD1.5 workflow JSON is reduced from 23.7kb (798 lines) to 10.9kb (407 lines).
- Default tiled upscale workflow JSON is reduced from 102.7kb (3341 lines) to 51.9kb (1774 lines).
The trade-off is that we need to reference node templates to get things like the field type and other things. In practice, this is a non-issue, because we need a node template to do anything with a node anyways.
- Field types are not included in the workflow. They are always pulled from the node templates.
The field type is now properly an internal implementation detail and we can change it as needed. Previously this would require a migration for the workflow itself. With the v3 schema, the structure of a field type is an internal implementation detail that we are free to change as we see fit.
- Workflow nodes no long have an `outputs` property and there is no longer such a thing as a `FieldOutputInstance`. These are only on the templates.
These were never referenced at a time when we didn't also have the templates available, and there'd be no reason to do so.
- Node width and height are no longer stored in the node.
These weren't used. Also, per https://reactflow.dev/api-reference/types/node, we shouldn't be programmatically changing these properties. A future enhancement can properly add node resizing.
- `nodeTemplates` slice is merged back into `nodesSlice` as `nodes.templates`. Turns out it's just a hassle having these separate in separate slices.
- Workflow migration logic updated to support the new schema. V1 workflows migrate all the way to v3 now.
- Changes throughout the nodes code to accommodate the above changes.
We have two different classes named `ModelInfo` which might need to be used by API consumers. We need to export both but have to deal with this naming collision.
The `ModelInfo` I've renamed here is the one that is returned when a model is loaded. It's the object least likely to be used by API consumers.
Replace `delete_on_startup: bool` & associated logic with `ephemeral: bool` and `TemporaryDirectory`.
The temp dir is created inside of `output_dir`. For example, if `output_dir` is `invokeai/outputs/tensors/`, then the temp dir might be `invokeai/outputs/tensors/tmpvj35ht7b/`.
The temp dir is cleaned up when the service is stopped, or when it is GC'd if not properly stopped.
In the event of a catastrophic crash where the temp files are not cleaned up, the user can delete the tempdir themselves.
This situation may not occur in normal use, but if you kill the process, python cannot clean up the temp dir itself. This includes running the app in a debugger and killing the debugger process - something I do relatively often.
Tests updated.
- The default is to not delete on startup - feels safer.
- The two services using this class _do_ delete on startup.
- The class has "ephemeral" removed from its name.
- Tests & app updated for this change.
`_delete_all` logged how many items it deleted, and had to be called _after_ service start bc it needed access to logger.
Move the logger call to the startup method and return the the deleted stats from `_delete_all`. This lets `_delete_all` be called at any time.
Turns out they are just different enough in purpose that the implementations would be rather unintuitive. I've made a separate ObjectSerializer service to handle tensors and conditioning.
Refined the class a bit too.
Turns out `ItemStorageABC` was almost identical to `PickleStorageBase`. Instead of maintaining separate classes, we can use `ItemStorageABC` for both.
There's only one change needed - the `ItemStorageABC.set` method must return the newly stored item's ID. This allows us to let the service handle the responsibility of naming the item, but still create the requisite output objects during node execution.
The naming implementation is improved here. It extracts the name of the generic and appends a UUID to that string when saving items.
- New generic class `PickleStorageBase`, implements the same API as `LatentsStorageBase`, use for storing non-serializable data via pickling
- Implementation `PickleStorageTorch` uses `torch.save` and `torch.load`, same as `LatentsStorageDisk`
- Add `tensors: PickleStorageBase[torch.Tensor]` to `InvocationServices`
- Add `conditioning: PickleStorageBase[ConditioningFieldData]` to `InvocationServices`
- Remove `latents` service and all `LatentsStorage` classes
- Update `InvocationContext` and all usage of old `latents` service to use the new services/context wrapper methods
This class works the same way as `WithMetadata` - it simply adds a `board` field to the node. The context wrapper function is able to pull the board id from this. This allows image-outputting nodes to get a board field "for free", and have their outputs automatically saved to it.
This is a breaking change for node authors who may have a field called `board`, because it makes `board` a reserved field name. I'll look into how to avoid this - maybe by naming this invoke-managed field `_board` to avoid collisions?
Supporting changes:
- `WithBoard` is added to all image-outputting nodes, giving them the ability to save to board.
- Unused, duplicate `WithMetadata` and `WithWorkflow` classes are deleted from `baseinvocation.py`. The "real" versions are in `fields.py`.
- Remove `LinearUIOutputInvocation`. Now that all nodes that output images also have a `board` field by default, this node is no longer necessary. See comment here for context: https://github.com/invoke-ai/InvokeAI/pull/5491#discussion_r1480760629
- Without `LinearUIOutputInvocation`, the `ImagesInferface.update` method is no longer needed, and removed.
Note: This commit does not bump all node versions. I will ensure that is done correctly before merging the PR of which this commit is a part.
Note: A followup commit will implement the frontend changes to support this change.
- The config is already cached by the config class's `get_config()` method.
- The config mutates itself in its `root_path` property getter. Freezing the class makes any attempt to grab a path from the config error. Unfortunately this means we cannot easily freeze the class without fiddling with the inner workings of `InvokeAIAppConfig`, which is outside the scope here.
Update all invocations to use the new context. The changes are all fairly simple, but there are a lot of them.
Supporting minor changes:
- Patch bump for all nodes that use the context
- Update invocation processor to provide new context
- Minor change to `EventServiceBase` to accept a node's ID instead of the dict version of a node
- Minor change to `ModelManagerService` to support the new wrapped context
- Fanagling of imports to avoid circular dependencies
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Added new tooltip popovers and updated copy of existing ones
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Merge Plan
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
Release - Invoke 3.7.0
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Invoke 3.7.0 Release
## QA Instructions, Screenshots, Recordings
Test Installer:
[InvokeAI-installer-v3.7.0.zip](https://github.com/invoke-ai/InvokeAI/files/14298200/InvokeAI-installer-v3.7.0.zip)
<!--
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## Merge Plan
Merge once approved
<!--
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## Added/updated tests?
- [ ] Yes
- [X] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
1. Release on PyPi
2. Release on GitHub
3. Announce on Discord
With these changes, the Docker image can be built and executed
successfully on hosts with AMD devices with ROCm acceleration.
Previously, a ROCm-enabled version of torch would be installed, but
later removed during installation of InvokeAI itself. This was caused by
InvokeAI needing a newer torch version than was previously installed.
The fix consists of multiple components:
* Update the hardcoded versions of torch and torchvision to the versions
currently used in pyproject.toml, so that a new version need not be
installed during installation of InvokeAI.
* Specify --extra-index-url on installation of InvokeAI so that even if
a verison mismatch occurs, the correct torch version should still be
installed. This also necessitates changing --index-url to
--extra-index-url for the Torch repo. Otherwise non-torch dependencies
would not be found.
* In run.sh, build the image for the selected service.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Merge Plan
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- "This must be squash-merged when approved"
- "DO NOT MERGE - I will rebase and tidy commits before merging"
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merged"
A merge plan is particularly important for large PRs or PRs that touch
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
* new workflow tab UI - still using shared state with workflow editor tab
* polish workflow details
* remove workflow tab, add edit/view mode to workflow slice and get that working to switch between within editor tab
* UI updates for view/edit mode
* cleanup
* add warning to view mode
* lint
* start with isTouched false
* working on styling mode toggle
* more UX iteration
* lint
* cleanup
* save original field values to state, add indicator if they have been changed and give user choice to reset
* lint
* fix import and commit translation
* dont switch to view mode when loading a workflow
* warns before clearing editor
* use folder icon
* fix(ui): track do not erase value when resetting field value
- When adding an exposed field, we need to add it to originalExposedFieldValues
- When removing an exposed field, we need to remove it from originalExposedFieldValues
- add `useFieldValue` and `useOriginalFieldValue` hooks to encapsulate related logic
* feat(ui): use IconButton for workflow view/edit button
* feat(ui): change icon for new workflow
It was the same as the workflow tab icon, confusing bc you think it's going to somehow take you to the tab.
* feat(ui): use render props for NewWorkflowConfirmationAlertDialog
There was a lot of potentially sensitive logic shared between the new workflow button and menu items. Also, two instances of ConfirmationAlertDialog.
Using a render prop deduplicates the logic & components
* fix(ui): do not mark workflow touched when loading workflow
This was occurring because the `nodesChanged` action is called by reactflow when loading a workflow. Specifically, it calculates and sets the node dimensions as it loads.
The existing logic set `isTouched` whenever this action was called.
The changes reactflow emits have types, and we can use the change types and data to determine if a change should result in the workflow being marked as touched.
* chore(ui): lint
* chore(ui): lint
* delete empty file
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Methods `get_node` and `complete` were typed as returning a dynamically created unions `InvocationsUnion` and `InvocationOutputsUnion`, respectively.
Static type analysers cannot work with dynamic objects, so these methods end up as effectively un-annotated, returning `Unknown`.
They now return `BaseInvocation` and `BaseInvocationOutput`, respectively, which are the superclasses of all members of each union. This gives us the best type annotation that is possible.
Note: the return types of these methods are never introspected, so it doesn't really matter what they are at runtime.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ X ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ X ] No, because: It's small
## Have you updated all relevant documentation?
- [ ] Yes
- [ X ] No
## Description
This pulls out some of the updates from the WIP Seamless branch that has
yet to be completed, and hardcodes values that are exposed in that
branch. Given that seamless currently does not generate seamless
textures, and this fix results in seamless outputs, it's an improvement
even if it doesn't resolve this in a "perfect" way that exposes all
variables to the end user.
better over perfect.
* remove thunk for receivedOpenApiSchema and use RTK query instead. add loading state for exposed fields
* clean up
* ignore any
* fix(ui): do not log on canceled openapi.json queries
- Rely on RTK Query for the `loadSchema` query by providing a custom `jsonReplacer` in our `dynamicBaseQuery`, so we don't need to manage error state.
- Detect when the query was canceled and do not log the error message in those situations.
* feat(ui): `utilitiesApi.endpoints.loadSchema` -> `appInfoApi.endpoints.getOpenAPISchema`
- Utilities is for server actions, move this to `appInfo` bc it fits better there.
- Rename to match convention for HTTP GET queries.
- Fix inverted logic in the `matchRejected` listener (typo'd this)
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
## What type of PR is this? (check all applicable)
Release Invoke 3.6.3
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Invoke 3.6.3 Release
## QA Instructions, Screenshots, Recordings
Test the installer:
[InvokeAI-installer-v3.6.3.zip](https://github.com/invoke-ai/InvokeAI/files/14233359/InvokeAI-installer-v3.6.3.zip)
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## Merge Plan
Merge once approved
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## [optional] Are there any post deployment tasks we need to perform?
1. Release on PyPi & GitHub
2. Announce on Discord
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: it is text only, simple, and (hopefully) self-evident
## Have you updated all relevant documentation?
- [x] Yes - as far as I can grep.
- [ ] No
## Description
`.env.sample` was misspelled as `env.sample` in a few places.
This changes documentation only. You may need to re-build/deploy docs,
I'm not sure.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
The change to memory session storage brings a subtle behaviour change.
Previously, we serialized and deserialized everything (e.g. field state,
invocation outputs, etc) constantly. The meant we were effectively
working with deep-copied objects at all time. We could mutate objects
freely without worrying about other references to the object.
With memory storage, objects are now passed around by reference, and we
cannot handle them in the same way.
This is problematic for nodes that mutate their own inputs. There are
two ways this causes a problem:
- An output is used as input for multiple nodes. If the first node
mutates the output object while `invoke`ing, the next node will get the
mutated object.
- The invocation cache stores live python objects. When a node mutates
an output pulled from the cache, the next node that uses the cached
object will get the mutated object.
The solution is to deep-copy a node's inputs as they are set,
effectively reproducing the same behaviour as we had with the SQLite
session storage. Nodes can safely mutate their inputs and those changes
never leave the node's scope.
## Related Tickets & Documents
<!--
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below.
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- Closes #5665
The root issue affects CLIP Skip because that node mutates its input
`ClipField`. Specifically, it increments `self.clip.skipped_layers` and
passes `self.clip` as its output. I don't know if there are any other
nodes that do this.
## QA Instructions, Screenshots, Recordings
Two issues to reproduce.
First is the caching issue:
Note the cache is enabled. Run this simple graph a couple times, and
check the outputs of the CLIP Skip node. You'll see the `skipped_layers`
value increasing each time.
Second is the nodes-sharing-inputs issue:
Note the cache is _disabled_. Run the graph a couple times and check the
outputs of the two CLIP Skip nodes. You'll see that one has the expected
value for `skipped_layers` and the other has double that.
Now update to the PR and try again. You should see `skipped_layers` is
the right value in all cases.
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## Merge Plan
This PR can be merged when approved. It needs a real review with
braintime.
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The change to memory session storage brings a subtle behaviour change.
Previously, we serialized and deserialized everything (e.g. field state, invocation outputs, etc) constantly. The meant we were effectively working with deep-copied objects at all time. We could mutate objects freely without worrying about other references to the object.
With memory storage, objects are now passed around by reference, and we cannot handle them in the same way.
This is problematic for nodes that mutate their own inputs. There are two ways this causes a problem:
- An output is used as input for multiple nodes. If the first node mutates the output object while `invoke`ing, the next node will get the mutated object.
- The invocation cache stores live python objects. When a node mutates an output pulled from the cache, the next node that uses the cached object will get the mutated object.
The solution is to deep-copy a node's inputs as they are set, effectively reproducing the same behaviour as we had with the SQLite session storage. Nodes can safely mutate their inputs and those changes never leave the node's scope.
Closes #5665
Currently translated at 74.4% (1054 of 1416 strings)
translationBot(ui): update translation (German)
Currently translated at 69.6% (986 of 1416 strings)
translationBot(ui): update translation (German)
Currently translated at 68.6% (972 of 1416 strings)
Co-authored-by: B N <berndnieschalk@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI
…elected
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Small bugfix: the installer would always print the latest stable version
as the one to be installed, even if a different one was selected. The
selected version would still be installed correctly. This PR fixes the
message.
## QA Instructions, Screenshots, Recordings
Select a pre-release version on install and observe the correct version
being printed. Compare to current behaviour to ascertain the fix.
## Merge Plan
- "This PR can be merged when approved"
## Added/updated tests?
- [ ] Yes
- [x] No
This has repeatedly shown itself useful in fixing install issues,
especially regarding pytorch CPU/GPU version, so there is little
downside to making this the default.
Performance impact of this should be negligible. Packages will
be reinstalled from pip cache if possible, and downloaded only if
necessary. Impact may be felt on slower disks.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because probably not needed
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
These are another minor dep updates that I was able to test without any
regressions. This will ensure we are up-to-date again.
The fixes are very minor, probably not noticeable in InvokeAI (at least
for diffusers) but it's still good to have them.
This is also to make sure that the RC is releasing with the latest
packages to ensure extended testing.
Greetings
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [x] Community Node Submission
## Description
- Adds BriaAI's new 1.4 model for background removal. Far superior
results from what I've tested compared to any other BG removal so far:
https://github.com/blessedcoolant/invoke_bria_rmbg
The stats service was logging error messages when attempting to retrieve stats for a graph that it wasn't tracking. This was rather noisy.
Instead of logging these errors within the service, we now will just raise the error and let the consumer of the service decide whether or not to log. Our usage of the service at this time is to suppress errors - we don't want to log anything to the console.
Note: With the improvements in the previous two commits, we shouldn't get these errors moving forward, but I still think this change is correct.
When an invocation is canceled, we consider the graph canceled. Log its graph's stats before resetting its graph's stats. No reason to not log these stats.
We also should stop the profiler at this point, because this graph is finished. If we don't stop it manually, it will stop itself and write the profile to disk when it is next started, but the resultant profile will include more than just its target graph.
Now we get both stats and profiles for canceled graphs.
When an invocation errored, we clear the stats for the whole graph. Later on, we check the graph for errors and see the failed invocation, and we consider the graph failed. We then attempts to log the stats for the failed graph.
Except now the failed graph has no stats, and the stats raises an error.
The user sees, in the terminal:
- An invocation error
- A stats error (scary!)
- No stats for the failed graph (uninformative!)
What the user should see:
- An invocation error
- Graph stats
The fix is simple - don't reset the graph stats when an invocation has an error.
Hardcode the options in the dropdown, don't rely on translators to fill this in.
Also, add a number of missing languages (Azerbaijani, Finnish, Hungarian, Swedish, Turkish).
Closes#5647
The alpha values in the UI are `0-1` but the backend wants `0-255`.
Previously, this was handled in `parseFIeldValue` when building the graph. In a recent release, field types were refactored and broke the alpha handling.
The logic for handling alpha values is moved into `ColorFieldInputComponent`, and `parseFieldValue` now just does no value transformations.
Though it would be a minor change, I'm leaving this function in because I don't want to change the rest of the logic except when necessary.
Closes#5616
Turns out the OpenAPI schema definition for a pydantic field with a `Literal` type annotation is different depending on the number of options.
When there is a single value (e.g. `Literal["foo"]`, this results in a `const` schema object. The schema parser didn't know how to handle this, and displayed a warning in the JS console.
This situation is now handled. When a `const` schema object is encountered, we interpret that as an `EnumField` with a single option.
I think this makes sense - if you had a truly constant value, you wouldn't make it a field, so a `const` must mean a dynamically generated enum that ended up with only a single option.
Currently translated at 40.6% (582 of 1433 strings)
translationBot(ui): update translation (Turkish)
Currently translated at 38.8% (557 of 1433 strings)
Co-authored-by: Ufuk Sarp Selçok <ilkel@live.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/tr/
Translation: InvokeAI/Web UI
- `ItemStorageMemory.get` now throws an `ItemNotFoundError` when the requested `item_id` is not found.
- Update docstrings in ABC and tests.
The new memory item storage implementation implemented the `get` method incorrectly, by returning `None` if the item didn't exist.
The ABC typed `get` as returning `T`, while the SQLite implementation typed `get` as returning `Optional[T]`. The SQLite implementation was referenced when writing the memory implementation.
This mismatched typing is a violation of the Liskov substitution principle, because the signature of the implementation of `get` in the implementation is wider than the abstract class's definition. Using `pyright` in strict mode catches this.
In `invocation_stats_default`, this introduced an error. The `_prune_stats` method calls `get`, expecting the method to throw if the item is not found. If the graph is no longer stored in the bounded item storage, we will call `is_complete()` on `None`, causing the error.
Note: This error condition never arose the SQLite implementation because it parsed the item with pydantic before returning it, which would throw if the item was not found. It implicitly threw, while the memory implementation did not.
The `getIntermediatesCount` query is set to `refetchOnMountOrArgsChange`. The intention was for when the settings modal opens (i.e. mounts), the `getIntermediatesCount` query is refetched. But it doesn't work - modals only mount once, there is no lazy rendering for them.
So we have to imperatively refetch, by refetching as we open the modal.
Closes#5639
* Port the command-line tools to use model_manager2
1.Reimplement the following:
- invokeai-model-install
- invokeai-merge
- invokeai-ti
To avoid breaking the original modeal manager, the udpated tools
have been renamed invokeai-model-install2 and invokeai-merge2. The
textual inversion training script should continue to work with
existing installations. The "starter" models now live in
`invokeai/configs/INITIAL_MODELS2.yaml`.
When the full model manager 2 is in place and working, I'll rename
these files and commands.
2. Add the `merge` route to the web API. This will merge two or three models,
resulting a new one.
- Note that because the model installer selectively installs the `fp16` variant
of models (rather than both 16- and 32-bit versions as previous),
the diffusers merge script will choke on any huggingface diffuserse models
that were downloaded with the new installer. Previously-downloaded models
should continue to merge correctly. I have a PR
upstream https://github.com/huggingface/diffusers/pull/6670 to fix
this.
3. (more important!)
During implementation of the CLI tools, found and fixed a number of small
runtime bugs in the model_manager2 implementation:
- During model database migration, if a registered models file was
not found on disk, the migration would be aborted. Now the
offending model is skipped with a log warning.
- Caught and fixed a condition in which the installer would download the
entire diffusers repo when the user provided a single `.safetensors`
file URL.
- Caught and fixed a condition in which the installer would raise an
exception and stop the app when a request for an unknown model's metadata
was passed to Civitai. Now an error is logged and the installer continues.
- Replaced the LoWRA starter LoRA with FlatColor. The former has been removed
from Civitai.
* fix ruff issue
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Seems we elected to convert checkpoints into .bin files when we set it
up. This doesn't seem to corrupt them anymore.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Merge Plan
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Initially I wanted to show how many sessions were being deleted. In hindsight, this is not great:
- It requires extra logic in the migrator, which should be as simple as possible.
- It may be alarming to see "Clearing 224591 old sessions".
The app still reports on freed space during the DB startup logic.
* fix(ui): download image opens in new tab
In some environments, a simple `a` element cannot trigger a download of an image. Fetching the image directly can get around this and provide more reliable download functionality.
* use hook for imageUrlToBlob so token gets sent if needed
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
This substantially reduces the time spent encoding PNGs. In workflows with many image outputs, this is a drastic improvement.
For a tiled upscaling workflow going from 512x512 to a scale factor of 4, this can provide over 15% speed increase.
This allows the stats to be written to disk as JSON and analyzed.
- Add dataclasses to hold stats.
- Move stats pretty-print logic to `__str__` of the new `InvocationStatsSummary` class.
- Add `get_stats` and `dump_stats` methods to `InvocationStatsServiceBase`.
- `InvocationStatsService` now throws if stats are requested for a session it doesn't know about. This avoids needing to do a lot of messy null checks.
- Update `DefaultInvocationProcessor` to use the new stats methods and suppresses the new errors.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Small PR to allow users to pass in a civit api key via config options
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
* redo top panel of workflow editor
* add checkbox option to save to project, integrate save-as flow into first time saving workflow
* remove log
* remove workflowLibrary as a feature that can be disabled
* lint
* feat(ui): make SaveWorkflowAsDialog a singleton
Fixes an issue where the workflow name would erroneously be an empty string (which it should show the current workflow name).
Also makes it easier to interact with this component.
- Extract the dialog state to a hook
- Render the dialog once in `<NodeEditor />`
- Use the hook in the various buttons that should open the dialog
- Fix a few wonkily named components (pre-existing issue)
* fix(ui): when saving a never-before-saved workflow, do not append " (copy)" to the name
* fix(ui): do not obscure workflow library button with add node popover
This component is kinda janky :/ the popover content somehow renders invisibly over the button. I think it's related to the `<PopoverAnchor />.
Need to redo this in the future, but for now, making the popover render lazily fixes this.
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Adds adds ctrl/meta + scroll to change brush size on canvas.
* changed hotkeys
* new hotkey as an additional
* lint fixed"
* added ctrl scroll and removed hotkey
* using
* added fix
* feedbck_changes
* brush size change logic
* feat(ui): also check for meta key when modifying brush size
* feat(ui): add comment linking to where brush size algo was determined
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This brings `docs/other/CONTRIBUTORS.md` into sync with collaborator
roles in Discord as of January 27, 2024.
## Related Tickets & Documents
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N/A
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## Merge Plan
Merge when approved.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Currently translated at 10.5% (151 of 1426 strings)
translationBot(ui): update translation (Turkish)
Currently translated at 8.1% (116 of 1426 strings)
translationBot(ui): update translation (Turkish)
Currently translated at 6.6% (95 of 1426 strings)
Co-authored-by: Ufuk Sarp Selçok <ilkel@live.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/tr/
Translation: InvokeAI/Web UI
It doesn't work now that the theme is external. I'm not sure how to fix it and not sure if it really did much (I don't think I ever got autocomplete...). Maybe it can be implemented in `@invoke-ai/ui-library`.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
- Update docs to make link to automated installer easier to find
- Fixed issue in SDXL + refiner example workflow
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
Read over docs changes
<!--
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## Merge Plan
Merge when approved
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## [optional] Are there any post deployment tasks we need to perform?
Deploy new docs
…y to distinguish what's being changed
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Merge Plan
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
* dont show duplicate toasts if workflow actions fail due to auth
* dynamic order by options based on projectId
* add endpointName to authtoast to makeit unique per endpoint
* lint
* update toast logic to check based on endpoint name w type safety
* fix save as endpoit name
* lint
* fix type
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
Invoke v3.6.2 release
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Invoke v3.6.2
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
pull
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
[InvokeAI-installer-v3.6.2.zip](https://github.com/invoke-ai/InvokeAI/files/14046191/InvokeAI-installer-v3.6.2.zip)
* retain id through workflow state so that we correctly update or save new
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
Invoke 3.6.1 release
## QA Instructions, Screenshots, Recordings
[InvokeAI-installer-v3.6.1.zip](https://github.com/invoke-ai/InvokeAI/files/14041411/InvokeAI-installer-v3.6.1.zip)
<!--
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## Merge Plan
This PR can be merged when approved
## [optional] Are there any post deployment tasks we need to perform?
PyPI Release & GitHub Release
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Have you updated all relevant documentation?
- [x] No
## Description
- This adds the newly released Depth Anything to InvokeAI. A new node
`Depth Anything Processor` has been added to generate depth maps using
this new technique. https://depth-anything.github.io
- All related checkpoints will be downloaded automatically on first
boot. The `DinoV2` models will be loaded to your torch cache dir and the
checkpoints pertaining to Depth Anything will be downloaded to
`any/annotators/depth_anything`.
- Alternatively you can find the checkpoints here and download them to
that folder:
https://huggingface.co/spaces/LiheYoung/Depth-Anything/tree/main/checkpoints
- This depth map can be used with any depth ControlNet model out there
but the folks at DepthAnything have also released a custom fine tuned
ControlNet model. From my limited testing, I still prefer the original
depth model because this one seems to be producing weird artifacts. Not
sure if that is a specific problem to Invoke or just the model itself.
I'll test more later. Place these in your controlnet folder like your
other ControlNets. You can get that here:
https://huggingface.co/spaces/LiheYoung/Depth-Anything/tree/main/checkpoints_controlnet
- Also available in the LinearUI
- DepthAnything has three models `large`, `base` and `small` -- I've
defaulted the processor to small but a user can change to the large
model if they wish to do so. Small is way faster but obviously somewhat
of a lesser quality.
- DepthAnything is now the default processor for depth controlnet
models.
## Screenshots
## Merge Plan
DO NOT MERGE YET. Test it first and I'm sure the model caching can be
done better. Coz I don't think I've done that at all. I would appreciate
if @brandonrising or @lstein or anyone can take a look at that part of
it.
* feat: ✨ "Remix Image" option on images
Adds a new "remix image" option where applicable, recalls all metadata except the seed
* refactor: 🚨 lint code
* feat: ✨ "Remix Image" option on images
Adds a new "remix image" option where applicable, recalls all metadata except the seed
* refactor: 🚨 lint code
* feat: ✨ add new remix hotkey to hotkeys modal
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Remove `trim()` from model identifier schema, which prevented parsed model identifiers from matching.
The root issue here is that model names are identifiers. This will be resolved in the model manager refactor.
Closes#5556
- Bump `@invoke-ai/ui` for updated styles
- Update regex to parse prompts with newlines
- Update styling of overlay button when prompt has an error
- Fix bug where loading and error state sometimes weren't cleared
We had a one-behind issue with recalling metadata items that had a model.
For example, when recalling LoRAs, we check against the current main model to decide whether or not the requested LoRA is compatible and may be recalled.
When recalling all params, we are often also recalling the main model, but the compat logic didn't compare against this new main model.
The logic is updated to check against the new main model, if one is being set.
Closes#5512
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Description
Update UI README
## Merge Plan
This PR can be merged when approved
<!--
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The frontend docs should just be in the frontend. This is a standard practice for monorepos with developer information for specific packages within the monorepo.
The Ideal Size node is useful for High-Res Optimization as it gives the optimum size for creating an initial generation with minimal artifacts (duplication and other strangeness) from today's models.
After inclusion, front end graph generation can be simplified by offloading calculations for HRO initial generation to this node.
The previous method wasn't totally foolproof, and locales/assets were cached.
To solve this once and for all (famous last words, I know), we can subclass `StaticFiles` and use maximally strict no-caching headers to disable caching on all static files.
Currently translated at 97.3% (1365 of 1402 strings)
translationBot(ui): update translation (Italian)
Currently translated at 97.3% (1365 of 1402 strings)
translationBot(ui): update translation (Italian)
Currently translated at 97.3% (1365 of 1402 strings)
translationBot(ui): update translation (Italian)
Currently translated at 97.3% (1365 of 1402 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
* resolved conflicts
* changed logo and some design changes
* feedback changes
* resolved conflicts
* changed logo and some design changes
* feedback changes
* lint fixed
* added translations
* some requested changes done
* all feedback changes done and replace links in settingsmenu comp
* fixed the gap between deps verisons & chnaged heights
* feat(ui): minor about modal styling
* feat(ui): tag app endpoints with FetchOnReconnect
* fix(ui): remove unused translation string
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Use each language's own language for their option in the language select. This falls back to the english translation if the language name isn't translated.
* add basic functionality for model metadata fetching from hf and civitai
* add storage
* start unit tests
* add unit tests and documentation
* add missing dependency for pytests
* remove redundant fetch; add modified/published dates; updated docs
* add code to select diffusers files based on the variant type
* implement Civitai installs
* make huggingface parallel downloading work
* add unit tests for model installation manager
- Fixed race condition on selection of download destination path
- Add fixtures common to several model_manager_2 unit tests
- Added dummy model files for testing diffusers and safetensors downloading/probing
- Refactored code for selecting proper variant from list of huggingface repo files
- Regrouped ordering of methods in model_install_default.py
* improve Civitai model downloading
- Provide a better error message when Civitai requires an access token (doesn't give a 403 forbidden, but redirects
to the HTML of an authorization page -- arrgh)
- Handle case of Civitai providing a primary download link plus additional links for VAEs, config files, etc
* add routes for retrieving metadata and tags
* code tidying and documentation
* fix ruff errors
* add file needed to maintain test root diretory in repo for unit tests
* fix self->cls in classmethod
* add pydantic plugin for mypy
* use TestSession instead of requests.Session to prevent any internet activity
improve logging
fix error message formatting
fix logging again
fix forward vs reverse slash issue in Windows install tests
* Several fixes of problems detected during PR review:
- Implement cancel_model_install_job and get_model_install_job routes
to allow for better control of model download and install.
- Fix thread deadlock that occurred after cancelling an install.
- Remove unneeded pytest_plugins section from tests/conftest.py
- Remove unused _in_terminal_state() from model_install_default.
- Remove outdated documentation from several spots.
- Add workaround for Civitai API results which don't return correct
URL for the default model.
* fix docs and tests to match get_job_by_source() rather than get_job()
* Update invokeai/backend/model_manager/metadata/fetch/huggingface.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Call CivitaiMetadata.model_validate_json() directly
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Second round of revisions suggested by @ryanjdick:
- Fix type mismatch in `list_all_metadata()` route.
- Do not have a default value for the model install job id
- Remove static class variable declarations from non Pydantic classes
- Change `id` field to `model_id` for the sqlite3 `model_tags` table.
- Changed AFTER DELETE triggers to ON DELETE CASCADE for the metadata and tags tables.
- Made the `id` field of the `model_metadata` table into a primary key to achieve uniqueness.
* Code cleanup suggested in PR review:
- Narrowed the declaration of the `parts` attribute of the download progress event
- Removed auto-conversion of str to Url in Url-containing sources
- Fixed handling of `InvalidModelConfigException`
- Made unknown sources raise `NotImplementedError` rather than `Exception`
- Improved status reporting on cached HuggingFace access tokens
* Multiple fixes:
- `job.total_size` returns a valid size for locally installed models
- new route `list_models` returns a paged summary of model, name,
description, tags and other essential info
- fix a few type errors
* consolidated all invokeai root pytest fixtures into a single location
* Update invokeai/backend/model_manager/metadata/metadata_store.py
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* Small tweaks in response to review comments:
- Remove flake8 configuration from pyproject.toml
- Use `id` rather than `modelId` for huggingface `ModelInfo` object
- Use `last_modified` rather than `LastModified` for huggingface `ModelInfo` object
- Add `sha256` field to file metadata downloaded from huggingface
- Add `Invoker` argument to the model installer `start()` and `stop()` routines
(but made it optional in order to facilitate use of the service outside the API)
- Removed redundant `PRAGMA foreign_keys` from metadata store initialization code.
* Additional tweaks and minor bug fixes
- Fix calculation of aggregate diffusers model size to only count the
size of files, not files + directories (which gives different unit test
results on different filesystems).
- Refactor _get_metadata() and _get_download_urls() to have distinct code paths
for Civitai, HuggingFace and URL sources.
- Forward the `inplace` flag from the source to the job and added unit test for this.
- Attach cached model metadata to the job rather than to the model install service.
* fix unit test that was breaking on windows due to CR/LF changing size of test json files
* fix ruff formatting
* a few last minor fixes before merging:
- Turn job `error` and `error_type` into properties derived from the exception.
- Add TODO comment about the reason for handling temporary directory destruction
manually rather than using tempfile.tmpdir().
* add unit tests for reporting HTTP download errors
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Per user feedback, this is preferrable to letting them expand when the window grows.
Also bumps `react-resizable-panels` now that one of my PRs is merged to fix an issue.
## What type of PR is this? (check all applicable)
Release v3.6.0
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Invoke v3.6.0
## QA Instructions, Screenshots, Recordings
[InvokeAI-installer-v3.6.0.zip](https://github.com/invoke-ai/InvokeAI/files/13923761/InvokeAI-installer-v3.6.0.zip)
## [optional] Are there any post deployment tasks we need to perform?
1. Release on PyPi
2. Release on GitHub
3. Announce in #releases
* feat: allow bfloat16 to be configurable in invoke.yaml
* fix: `torch_dtype()` util
- Use `choose_precision` to get the precision string
- Do not reference deprecated `config.full_precision` flat (why does this still exist?), if a user had this enabled it would override their actual precision setting and potentially cause a lot of confusion.
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
- Add various brand images, organise images
- Create favicon for docs pages (light blue version of key logo)
- Rename app title to `Invoke - Community Edition`
Add `FetchOnReconnect` tag, tagging relevant queries with it. This tag is invalidated in the socketConnected listener, when it is determined that the queue changed.
- Add checks to the "recovery" logic for socket connect events to reduce the number of network requests.
- Remove the `isInitialized` state from `systemSlice` and make it a nanostore local to the socketConnected listener. It didn't need to be global state. It's also now more clearly named `isFirstConnection`.
- Export the queue status selector (minor improvement, memoizes it correctly).
## What type of PR is this? (check all applicable)
Release v3.6.0rc6
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Release candidate $6
## QA Instructions, Screenshots, Recordings
[InvokeAI-installer-v3.6.0rc6.zip](https://github.com/invoke-ai/InvokeAI/files/13890206/InvokeAI-installer-v3.6.0rc6.zip)
## Merge Plan
Merge when approved
## [optional] Are there any post deployment tasks we need to perform?
Release on PyPi & Github
- Fixed a bug where after you load more, changing boards doesn't work. The offset and limit for the list image query had some wonky logic, now resolved.
- Addressed major lag in gallery when selecting an image.
Both issues were related to the useMultiselect and useGalleryImages hooks, which caused every image in the gallery to re-render on whenever the selection changed. There's no way to memoize away this - we need to know when the selection changes. This is a longstanding issue.
The selection is only used in a callback, though - the onClick handler for an image to select it (or add it to the existing selection). We don't really need the reactivity for a callback, so we don't need to listen for changes to the selection.
The logic to handle multiple selection is moved to a new `galleryImageClicked` listener, which does all the selection right when it is needed.
The result is that gallery images no long need to do heavy re-renders on any selection change.
Besides the multiselect click handler, there was also inefficient use of DND payloads. Previously, the `IMAGE_DTOS` type had a payload of image DTO objects. This was only used to drag gallery selection into a board. There is no need to hold onto image DTOs when we have the selection state already in redux. We were recalculating this payload for every image, on every tick.
This payload is now just the board id (the only piece of information we need for this particular DND event).
- I also removed some unused DND types while making this change.
There was a lot of convoluted, janky logic related to trying to not mount the context menu's portal until its needed. This was in the library where the component was originally copied from.
I've removed that and resolved the jank, at the cost of there being an extra portal for each instance of the context menu. Don't think this is going to be an issue. If it is, the whole context menu could be refactored to be a singleton.
* ci: add docker build timout; log free space on runner before and after build
* docker: bump frontend builder to node=20.x; skip linting on build
* chore: gitignore .pnpm-store
* update code owners for docker and CI
---------
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
I was troubleshooting a hotkeys issue on canvas and thought I had broken the tool logic in a past change so I redid it moving it to nanostores. In the end, the issue was an upstream but with the hotkeys library, but I like having tool in nanostores so I'm leaving it.
It's ephemeral interaction state anyways, doesn't need to be in redux.
There's a challenge to accomplish this due to our slice structure - the model is stored in `generationSlice`, but `canvasSlice` also needs to have awareness of it. For example, when the model changes, the canvas slice doesn't know what the previous model was, so it doesn't know whether or not to optimize the size.
This means we need to lift the "should we optimize size" information up. To do this, the `modelChanged` action creator accepts the previous model as an optional second arg.
Now the canvas has access to both the previous model and new model selection, and can decide whether or not it should optimize its size setting in the same way that the generation slice does.
Closes #5452
For some reason `ReturnType<typeof useListImagesQuery>` isn't working correctly, and destructuring `queryResult` it results in `any`, when the hook is used.
I've removed the explicit return typing so that consumers of the hook get correct types.
Organise deps into ~3 categories:
- Core generation dependencies, pinned for reproducible builds.
- Core application dependencies, pinned for reproducible builds.
- Auxiliary dependencies, pinned only if necessary.
I pinned / bumped these to latest:
- `controlnet_aux`
- `fastapi`
- `fastapi-events`
- `huggingface-hub`
- `numpy`
- `python-socketio`
- `torchmetrics`
- `transformers`
- `uvicorn`
I checked the release notes for these and didn't see any breaking changes that would affect us. There is a `fastapi` breaking change in v108 related to background tasks but it doesn't affect us.
I tested on a fresh venv. The app still works and I can generate on macOS.
Hopefully, enforcing explicit pinned versions will reduce the issues where people get CPU torch.
It also means we should periodically bump versions up to ensure we don't get too far behind on our dependencies and have to do painful upgrades.
Workflow building would fail when a current image node was in the workflow due to the strict validation.
So we need to use the other workflow builder util first, which strips out extraneous data.
This bug was introduced during an attempt to optimize the workflow building logic, which was causing slowdowns on the workflow editor.
* do not show toast if 403 is triggered by lack of image access
* remove log
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
Release - InvokeAI v3.5.0rc5
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Release - InvokeAI v3.5.0rc5
## QA Instructions, Screenshots, Recordings
[InvokeAI-installer-v3.6.0rc5.zip](https://github.com/invoke-ai/InvokeAI/files/13863661/InvokeAI-installer-v3.6.0rc5.zip)
## [optional] Are there any post deployment tasks we need to perform?
Releasee on PyPi & GitHub
* feat(ui): get rid of convoluted socket vs appSocket redux actions
There's no need to have `socket...` and `appSocket...` actions.
I did this initially due to a misunderstanding about the sequence of handling from middleware to reducers.
* feat(ui): bump deps
Mainly bumping to get latest `redux-remember`.
A change to socket.io required a change to the types in `useSocketIO`.
* chore(ui): format
* feat(ui): add error handling to redux persistence layer
- Add an error handler to `redux-remember` config using our logger
- Add custom errors representing storage set and get failures
- Update storage driver to raise these accordingly
- wrap method to clear idbkeyval storage and tidy its logic up
* feat(ui): add debuggingLoggerMiddleware
This simply logs every action and a diff of the state change.
Due to the noise this creates, it's not added by default at all. Add it to the middlewares if you want to use it.
* feat(ui): add $socket to window if in dev mode
* fix(ui): do not enable cancel hotkeys on inputs
* fix(ui): use JSON.stringify for ROARR logger serializer
A recent change to ROARR introduced limits to the size of data that will logged. This ends up making our logs far less useful. Change the serializer back to what it was previously.
* feat(ui): change diff util, update debuggerLoggerMiddleware
The previous diff library would present deleted things as `undefined`. Unfortunately, a JSON.stringify cycle will strip those values out. The ROARR logger does this and so the diffs end up being a lot less useful, not showing removed keys.
The new diff library uses a different format for the delta that serializes nicely.
* feat(ui): add migrations to redux persistence layer
- All persisted slices must now have a slice config, consisting of their initial state and a migrate callback. The migrate callback is very simple for now, with no type safety. It adds missing properties to the state. A future enhancement might be to model the each slice's state with e.g. zod and have proper validation and types.
- Persisted slices now have a `_version` property
- The migrate callback is called inside `redux-remember`'s `unserialize` handler. I couldn't figure out a good way to put this into the reducer and do logging (reducers should have no side effects). Also I ran into a weird race condition that I couldn't figure out. And finally, the typings are tricky. This works for now.
- `generationSlice` and `canvasSlice` both need migrations for the new aspect ratio setup, this has been added
- Stuff related to persistence has been moved in to `store.ts` for simplicity
* feat(ui): clean up StorageError class
* fix(ui): scale method default is now 'auto'
* feat(ui): when changing controlnet model, enable autoconfig
* fix(ui): make embedding popover immediately accessible
Prevents hotkeys from being captured when embeddings are still loading.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
The new select component appears to close itself before calling the
onchange handler. This short-circuits the autoconnect logic. Tweaked so
the ordering is correct.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Closes#5425
## QA Instructions, Screenshots, Recordings
bug should be fixed
<!--
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software specifications as well as any other pertinent information.
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## Merge Plan
This PR can be merged when approved
<!--
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- "This must be squash-merged when approved"
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The new select component appears to close itself before calling the onchange handler. This short-circuits the autoconnect logic. Tweaked so the ordering is correct.
Centralize the initial/min/max/etc values for all numerical params. We used this for some but at some point stopped updating it.
All numerical params now use their respective configs. Far fewer hardcoded values throughout the app now.
Also updated the config types a bit to better accommodate slider vs number input constraints.
- Use the virtuoso grid item container and list containers to calculate imagesPerRow, skipping manual compensation for padding of images
- Round the imagesPerRow instead of flooring - we often will end up with values like 4.99999 due to floating point precision
- Update `getDownImage` comments & logic to be clearer
- Use variables for the ids in query selectors, preventing future typos
- Only scroll if the new selected image is different from the prev one
- Fix preexisting bug where gallery network requests were duplicated when triggering infinite scroll
- Refactor `useNextPrevImage` to not use `state => state` as an input selector - logic split up into different hooks
- Remove use instant scroll for arrow key navigation - smooth scroll is janky when you hold the arrow down and it fires rapidly
- Move gallery nav hotkeys to GalleryImageGrid component, so they work whenever the gallery is open (previously didn't work on canvas or workflow editor tabs)
- Use nanostores for gallery grid refs instead of passing context with virtuoso's context feature, making it much simpler to do the imperative gallery nav
- General gallery hook/component cleanup
## What type of PR is this? (check all applicable)
Release v3.6.0rc4
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Release for v3.6.0rc4
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
[Uploading InvokeAI-installer-v3.6.0rc4.zip…](Installer Zip)
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Merge Plan
- This PR can be merged when approved
<!--
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merged"
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Release on PyPi & GitHub
Pending resolution of https://github.com/reduxjs/reselect/issues/635, we can patch `reselect` to use `lruMemoize` exclusively.
Pin RTK and react-redux versions too just to be safe.
This reduces the major GC events that were causing lag/stutters in the app, particularly in canvas and workflow editor.
## What type of PR is this? (check all applicable)
Release v3.6.0rc3
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [] Yes
- [X] No
## Description
Next release candidate
## Related Tickets & Documents
N/A
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## QA Instructions, Screenshots, Recordings
[Uploading InvokeAI-installer-v3.6.0rc3.zip…](Installer zip)
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This PR can be merged when approved
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- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Release on PyPI & Github
A bug that caused panels to be collapsed on a fresh indexedDb in was fixed in dd32c632cd, but this re-introduced a different bug that caused the panels to expand on window resize, if they were already collapsed.
Revert the previous change and instead add one imperative resize outside the observer, so that on startup, we set both panels to their minimum sizes.
* replace custom header with custom nav component to go below settings
* add option for custom gallery header
* add option for custom app info text on logo hover
* add data-testid for tabs
* remove descriptions
* lint
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
We are now using the lefthand vertical strip for the settings menu button. This is a good place for the status indicator.
Really, we only need to display something *if there is a problem*. If the app is processing, the progress bar indicates that.
For the case where the panels are collapsed, I'll add the floating buttons back in some form, and we'll indicate via those if the app is processing something.
just make it like a normal button - normal and hover state, no difference when its expanded. the icon clearly indicates this, and you see the extra components
On one hand I like the color but on the other it makes this divider a focus point, which doesn't really makes sense to me. I tried several shades but think it adds a bit too much distraction for your eyes.
There was an extra div, needed for the fullscreen file upload dropzone, that made styling the main app containers a bit awkward.
Refactor the uploader a bit to simplify this - no longer need so many app-level wrappers. Much cleaner.
Removed logic related to aspect ratio from the components.
When the main bbox changes, if the scale method is auto, the reducers will handle the scaled bbox size appropriately.
Somehow linking up the manual mode to the aspect ratio is tricky, and instead of adding complexity for a rarely-used mode, I'm leaving manual mode as fully manual.
Cannot figure out how to allow the bbox to be transformed when aspect ratio is locked from all handles. Only the bottom right handle works as expected.
As a workaround, when the aspect ratio is locked, you can only resize the bbox from the bottom right handle.
## What type of PR is this? (check all applicable)
Release
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
v3.6.0rc2 release
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
Test latest main & [Uploading
InvokeAI-installer-v3.6.0rc2.zip…](Installer zip)
## Merge Plan
PR can be merged immediately
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## Added/updated tests?
- [ ] Yes
- [X] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Publish release on PyPI and GitHub
- Do not _merge_ prompt and style prompt when concat is enabled - either use the prompt as style, or use the style directly.
- Set style prompt metadata correctly.
- Add metadata recall for style prompt.
`react-select` has some weird behaviour where if the value is `undefined`, it shows the last-selected value instead of nothing. Must fall back to `null`
Ensure workflow editor model selector component gets a value
This introduced some funky type issues related to ONNX models. ONNX doesn't work anyways (unmaintained). Instead of fixing the types to work with a non-working feature, ONNX is now removed entirely from the UI.
- Remove all refs to ONNX (and Olives)
- Fix some type issues
- Add ONNX nodes to the nodes denylist (so they are not visible in UI)
- Update VAE graph helper, which still had some ONNX logic. It's a very simple change and doesn't change any logic. Just removes some conditions that were for ONNX. I tested it and nothing broke.
- Regenerate types
- Fix prettier and eslint ignores for generated types
- Lint
* Udpater suggest db backup when installing RC
* Update invokeai_update.py to be more specific
* Update invokeai_update.py
* Update invokeai_update.py
* Update invokeai_update.py
* Update invokeai_update.py
* Update docker-compose.yml to bind local data path
* Update LOCAL_DATA_PATH in .env.sample
* Add fallback to INVOKEAI_ROOT envar if LOCAL_DATA_PATH not present.
* rename LOCAL_DATA_PATH to INVOKAI_LOCAL_ROOT
* Whoops, didnt mean to include this
* Update docker/docker-compose.yml
Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
* [chore] rename envar
* Apply suggestions from code review
---------
Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
- Prompt must have an open curly brace followed by a close curly brace to enable dynamic prompts processing
- If a the given prompt already had a dynamic prompt cached, do not re-process
- If processing is not needed, user may invoke immediately
- Invoke button shows loading state when dynamic prompts are processing, tooltip says generating
- Dynamic prompts preview icon in prompt box shows loading state when processing, tooltip says generating
- Support grid size of 8 on canvas
- Internal canvas math works on 8
- Update gridlines rendering to show 64 spaced lines and 32/16/8 when zoomed in
- Bbox manipulation defaults to grid of 64 - hold shift to get grid of 8
Besides being something we support internally, supporting 8 on canvas avoids a lot of hacky logic needed to work well with aspect ratios.
Canvas and non-canvas have separate width and height and need their own separate aspect ratios. In order to not duplicate a lot of aspect ratio logic, the components relating to image size have been modularized.
- Fix `weight` and `begin_step_percent`, the constraints were mixed up
- Add model validatort to ensure `begin_step_percent < end_step_percent`
- Bump version
## What type of PR is this? (check all applicable)
InvokeAI 3.6.0rc1 Release
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Update version & frontend build for Invoke v3.6.0rc1
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Upload release to PyPI & create release on GitHub
- Store workflow in nanostore as singleton instead of building for each consumer
- Debounce the build (already was indirectly debounced)
- When the workflow is needed, imperatively grab it from the nanostores, instead of letting react handle it via reactivity
This drastically reduces the computation needed when moving the cursor. It also correctly separates ephemeral interaction state from redux, where it is not needed.
Also removed some unused canvas state.
This uses the previous implementation of the memoization function in reselect. It's possible for the new weakmap-based memoization to cause memory leaks in certain scenarios, so we will avoid it for now.
## What type of PR is this? (check all applicable)
Release v3.5.1
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
InvokeAI v3.5.1 release
## [optional] Are there any post deployment tasks we need to perform?
1. Release on PyPi
2. Create GH release
3. Annonce on Discord
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
Add Tiled Upscaling to default workflows
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
If the user specifies `torch-sdp` as the attention type in `config.yaml`, we can go ahead and use it (if available) rather than always throwing an exception.
## What type of PR is this? (check all applicable)
- [X] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
To release 3.5.0 successfully, a front end build needed to be in the
repo so that it would be included in the invokeai package distributed on
PyPi.
This PR remove the frontend build and updates the frontend gitignore to
not include the build.
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [X] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
N/A
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: it's a simple fix
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
if there are two multi vector TI in a prompt eg `<ti-1> <ti-2>` with
ti-1 has vector size 16 and ti-2 has vector size 8 then the second one
uses the first ti_embedding.shape[0] and you get errors like eg
"<ti-2-!pad-8> is not found" because ti-2 only has vector size 8 but the
code is taking the wrong ti_embedding.shape[0]
## Related Tickets & Documents
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- Related Issue #
- Closes #
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
InvokeAI v3.5.0
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
3.5.0 release
## QA Instructions, Screenshots, Recordings
Test Installer:
[InvokeAI-installer-v3.5.0.zip](https://github.com/invoke-ai/InvokeAI/files/13776161/InvokeAI-installer-v3.5.0.zip)
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
* Update front end .gitignore & remove the fe build
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
For example, if PIL tries to open a *really* big image, it will raise an
exception to prevent reading a huge object into memory.
## Related Tickets & Documents
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-
https://discord.com/channels/1020123559063990373/1149513695567810630/1186200089149046804
## QA Instructions, Screenshots, Recordings
This should fix the error in the discord thread
<!--
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## Merge Plan
Can be merged when @Millu confirms it fixes the issue he ran into
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* add base definition of download manager
* basic functionality working
* add unit tests for download queue
* add documentation and FastAPI route
* fix docs
* add missing test dependency; fix import ordering
* fix file path length checking on windows
* fix ruff check error
* move release() into the __del__ method
* disable testing of stderr messages due to issues with pytest capsys fixture
* fix unsorted imports
* harmonized implementation of start() and stop() calls in download and & install modules
* Update invokeai/app/services/download/download_base.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* replace test datadir fixture with tmp_path
* replace DownloadJobBase->DownloadJob in download manager documentation
* make source and dest arguments to download_queue.download() an AnyHttpURL and Path respectively
* fix pydantic typecheck errors in the download unit test
* ruff formatting
* add "job cancelled" as an event rather than an exception
* fix ruff errors
* Update invokeai/app/services/download/download_default.py
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* use threading.Event to stop service worker threads; handle unfinished job edge cases
* remove dangling STOP job definition
* fix ruff complaint
* fix ruff check again
* avoid race condition when start() and stop() are called simultaneously from different threads
* avoid race condition in stop() when a job becomes active while shutting down
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
The graph library occasionally causes issues when the default graph changes substantially between versions and pydantic validation fails. See #5289 for an example.
We are not currently using the graph library, so we can disable it until we are ready to use it. It's possible that the workflow library will supersede it anyways.
* remove MacOS Sonoma check in devices.py
As of pytorch 2.1.0, float16 works with our MPS fixes on Sonoma, so the check is no longer needed.
* remove unused platform import
The project is no longer using yarn as a package manager and have moved
to pnpm, So I wanted to update the documentation on the contribution
page.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [] No, because:
I spoke with user: imic in the #dev-chat on discord.
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Merge Plan
- "This PR can be merged when approved"
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Added more default workflows to the workflow library
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
* add code to repopulate model config records after schema update
* reformat for ruff
* migrate model records using db cursor rather than the ModelRecordConfigService
* ruff fixes
* tweak exception reporting
* fix: build frontend in pypi-release workflow
This was missing, resulting in the 3.5.0rc1 having no frontend.
* fix: use node 18, set working directory
- Node 20 has a problem with `pnpm`; set it to Node 18
- Set the working directory for the frontend commands
* Don't copy extraneous paths into installer .zip
* feat(installer): delete frontend build after creating installer
This prevents an empty `dist/` from breaking the app on startup.
* feat: add python dist as release artifact, as input to enable publish to pypi
- The release workflow never runs automatically. It must be manually kicked off.
- The release workflow has an input. When running it from the GH actions UI, you will see a "Publish build on PyPi" prompt. If this value is "true", the workflow will upload the build to PyPi, releasing it. If this is anything else (e.g. "false", the default), the workflow will build but not upload to PyPi.
- The `dist/` folder (where the python package is built) is uploaded as a workflow artifact as a zip file. This can be downloaded and inspected. This allows "dry" runs of the workflow.
- The workflow job and some steps have been renamed to clarify what they do
* translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.
Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
* freeze yaml migration logic at upgrade to 3.5
* moved migration code to migration_3
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Hosted Weblate <hosted@weblate.org>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
Updater script pulls from PyPI instead of GitHub releases (this is why
the RC packages are having issues when updating through the launcher
script)
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [X] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you updated all relevant documentation?
- [x] Yes (N/A)
- [ ] No
## Description
This change enables the model probe to work with TI embeddings that have
the follow state_dict structure:
```python
{
"<any_key>": torch.Tensor(...), # where the tensor has shape (N, embedding_dim)
}
```
## QA Instructions, Screenshots, Recordings
I can't imagine an embedding format that would previously have passed
the model probe, and would now fail after this change. That being said,
I'll exercise a bunch of existing TIs before merging.
- [x] Exercise existing TI formats
## Added/updated tests?
- [ ] Yes
- [x] No : _We could really benefit from tests for all of the supported
TI formats... but I'm not taking on that project right now._
## What type of PR is this? (check all applicable)
- [X] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
As discussed with @psychedelicious , this PR changes the swagger label
on the model manager V2 routes to `model_manager_v2_unstable` in order
to warn community members that the API is liable to change.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Merge Plan
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Change CalculateImageTilesEvenSplitInvocation to have an overlap in
pixels rather than as a percentage of the tile. This makes it easier to
have predictable blending of the seams as you have a known overlap size.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Merge Plan
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## Added/updated tests?
- [x] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [x] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes -
https://github.com/invoke-ai/InvokeAI/pull/5007#discussion_r1378792615
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Simplify Docker image creation and execution to a single script that
spins up the right service in the docker compose file.
## Related Tickets & Documents
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below.
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- Depends on #5007
## QA Instructions, Screenshots, Recordings
N/A
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [x] No : same tests should work.
## [optional] Are there any post deployment tasks we need to perform?
Not to my knowledge.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
This was missing, resulting in the 3.5.0rc1 having no frontend.
## Related Tickets & Documents
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- Discord installer thread:
https://discord.com/channels/1020123559063990373/1149513695567810630/1185200427717898260
- Comments from here in the release chat:
https://discord.com/channels/1020123559063990373/1020123559831539744/1185004017521279007
## QA Instructions, Screenshots, Recordings
I've run this locally and it works (I commented out the final steps of
the workflow that do PyPi stuff to ensure I didn't accidentally deploy
something).
You can run the workflow locally with https://github.com/nektos/act.
Suggest using the `gh` CLI version, its very easy to set up if you have
the github CLI installed. Then you can run `gh act -W
.github/workflows/pypi-release.yml` to run the workflow locally in a
docker image.
I don't know this local action runner would actually release to PyPi -
as mentioned, I commented those steps out when testing - but it does
successfully do both frontend and backend builds.
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## Merge Plan
This needs @lstein 's approval.
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## [optional] Are there any post deployment tasks we need to perform?
Cut an RC2
- The release workflow never runs automatically. It must be manually kicked off.
- The release workflow has an input. When running it from the GH actions UI, you will see a "Publish build on PyPi" prompt. If this value is "true", the workflow will upload the build to PyPi, releasing it. If this is anything else (e.g. "false", the default), the workflow will build but not upload to PyPi.
- The `dist/` folder (where the python package is built) is uploaded as a workflow artifact as a zip file. This can be downloaded and inspected. This allows "dry" runs of the workflow.
- The workflow job and some steps have been renamed to clarify what they do
The VAE decode on linear graphs was getting cached. This caused some unexpected behaviour around image outputs.
For example, say you ran the exact same graph twice. The first time, you get an image written to disk and added to gallery. The second time, the VAE decode is cached and no image file is created. But, the UI still gets the graph complete event and selects the first image in the gallery. The second run does not add an image to the gallery.
There are probbably edge cases related to this - the UI does not expect this to happen. I'm not sure how to handle it any better in the UI.
The solution is to not cache VAE decode on the linear graphs, ever. If you run a graph twice in linear, you expect two images.
This simple change disables the node cache for terminal VAE decode nodes in all linear graphs, ensuring you always get images. If they graph was fully cached, all images after the first will be created very quickly of course.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This adds a probe for the SDXL LoRA format found in the wild at
https://civitai.com/models/224641.
## Related Tickets & Documents
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See discord message at:
https://discord.com/channels/1020123559063990373/1149510134058471514/1184982133912113182
## QA Instructions, Screenshots, Recordings
Try installing the SDXL LoRA at the URL given above.
## Merge Plan
This can be merged when approved.
## Added/updated tests?
- [ ] Yes
- [X] No : we do not yet have a comprehensive suite of models to test
probing on.
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This minor change adds the ability to filter the model lists returned by
V2 of the model manager using the model file format (e.g. "checkpoint").
Just thought this would be a useful feature.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Merge Plan
This can be merged when approved without any adverse effects.
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## Added/updated tests?
- [ ] Yes
- [X] No : minor feature - tested informally using the router API
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ x ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ x ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ x ] Yes
- [ ] No
## Description
This adds the Kapa assistant to our docs.
- "Reset Workflow Editor" -> "New Workflow"
- "New Workflow" gets nodes icon & is no longer danger coloured
- When creating a new workflow, if the current workflow has unsaved changes, you get a dialog asking for confirmation. If the current workflow is saved, it immediately creates a new workflow.
- "Download Workflow" -> "Save to File"
- "Upload Workflow" -> "Load from File"
- Moved "Load from File" up 1 in the menu
This model was a bit too strict, and raised validation errors when workflows we expect to *not* have an ID (eg, an embedded workflow) have one.
Now it strips unknown attributes, allowing those workflows to load.
- Handle an image file not existing despite being in the database.
- Add a simple pydantic model that tests only for the existence of a workflow's version.
- Check against this new model when migrating workflows, skipping if the workflow fails validation. If it succeeds, the frontend should be able to handle the workflow.
Currently translated at 98.1% (1340 of 1365 strings)
translationBot(ui): update translation (Russian)
Currently translated at 84.2% (1150 of 1365 strings)
translationBot(ui): update translation (Russian)
Currently translated at 83.1% (1135 of 1365 strings)
Co-authored-by: Васянатор <ilabulanov339@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ X ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ X ] No, because: dependency bump
## Have you updated all relevant documentation?
- [ ] Yes
- [ x ] No
## Description
Updating diffusers to .24 - fixes a few issues. Needs to be tested to
ensure things like our IP Adapter implementation don't break
## What type of PR is this? (check all applicable)
- [x] Refactor
- [x] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
This PR enhances our SQLite database with migration logic.
### `SQLiteMigrator` class
The new `SQLiteMigrator` class handles safely running database
migrations. It is initialized in the `SqliteDatabase` class's init, and
immediately runs all database migrations.
### `Migration` class
Migrations are reprsented by a `Migration` class, which has 3
attributes:
- `db_version: int`: The database version this migration results in.
- `app_version: str`: The semver app version this migration is run for.
- `migrate: Callable[[sqlite3.Cursor], None]`: A function that performs
the migration. It receives a cursor _only_, but can do anything it wants
to do. A convention is established for these functions.
All schema-creating SQL now lives in a `migrate` function. We haven't
needed to make any data migrations yet, but when we do, this will also
be handled within one of these callbacks.
### Migration Flow
First, migrations are registered with `SQLiteMigrator` with it's
`register_migration` method. This performs some basic checks of the
migration version.
After registering all migrations, they are run with the `run_migrations`
method. This does a few things:
- Creates a `version` table in the DB, if it doesn't already exist. This
table has `db_version INTEGER`, `app_version TEXT` and `migrated_at
DATETIME` columns.
- Sort the migrations by their `db_version`.
- Do some checks to see if we need a migration.
- Backs up the database (if it's a file database). The migration bails
out if this fails.
- Runs each migration. If there is a problem, restore from backup.
### Included Migrations
Migrations are in `invokeai/app/services/shared/sqlite/migrations`.
#### `migrate_1.py`
All\* schema SQL up to 3.4.0post2 is in `migration_1.py`. Running only
this migration should result in a database that is identical to the one
you get from starting up 3.4.0post2.
SQL in this migration is **idempotent** (same as it was when the SQL was
spread across the various services).
#### `migrate_2.py`
Schema changes through 3.5.0 (the upcoming release) are in
`migration_2.py`.
SQL in this migration is **not idempotent**. Future migrations need not
be idempotent, as the migration logic ensures each will only be run
once.
### \*Caveat - ItemStorage
This class provides a generic document-db-like interface for storing
objects. Our `graph_executions` and `graphs` tables are created and
managed by this service. This PR does not touch this class and therefore
does not touch either of those two tables.
We can decide how to handle those tables in the future as the need
arises.
### Change to Model Manager Metadata table
I noticed that there is a `model_manager_metadata` table which included
the app version, and whose `version` property wasn't accessed outside
the service.
I believe the new `version` table fulfills the purpose of this table,
and have removed it.
@lstein Please let me know if this is not right.
## QA Instructions, Screenshots, Recordings
1. Case 1 - Upgrade
- Back up your 3.4.0post2 database
- Run this PR
- It should upgrade your database and everything should work exactly
like it did before
2. Case 2 - New Install
- Move your database out of the invoke root so that when the app starts,
it creates a new one
- Run this PR
- It should work just like a new install
3. Case 3 - With an In-Memory Database
- Enable the in-memory memory database (set `use_memory_db` under
`Paths` in `invokeai.yaml` to `true`)
- Run this PR
- It should work just like a new install
## Added/updated tests?
- [x] Yes: Fairly comprehensive tests are added for the
`SQLiteMigrator`.
- [ ] No : _please replace this line with details on why tests
have not been included_
- use simpler pattern for migration dependencies
- move SqliteDatabase & migration to utility method `init_db`, use this in both the app and tests, ensuring the same db schema is used in both
This fixes a problem with `Annotated` which prevented us from using pydantic's `Field` to specify a discriminator for a union. We had to use FastAPI's `Body` as a workaround.
* selector added
* ref and useeffect added
* scrolling done using useeffect
* fixed scroll and changed the ref name
* fixed scroll again
* created hook for scroll logic
* feat(ui): debounce metadata fetch by 300ms
This vastly reduces the network requests when using the arrow keys to quickly skim through images.
* feat(ui): extract logic to determine virtuoso scrollToIndex align
This needs to be used in `useNextPrevImage()` to ensure the scrolling puts the image at the top or bottom appropriately
* feat(ui): add debounce to image workflow hook
This was spamming network requests like the metadata query
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Invocations now have a classification:
- Stable: LTS
- Beta: LTS planned, API may change
- Prototype: No LTS planned, API may change, may be removed entirely
The `@invocation` decorator has a new arg `classification`, and an enum `Classification` is added to `baseinvocation.py`.
The default is Stable; this is a non-breaking change.
The classification is presented in the node header as a hammer icon (Beta) or flask icon (prototype).
The icon has a tooltip briefly describing the classification.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
1. The new model manager sqlite3-based configuration record storage
system is automatically populated with probed values from existing
models found in the models path when `invokeai-web` starts up for the
first time. However, the user's customization of these models in
`invokeai.yaml`, including such things as the prediction type and model
description, are not automatically copied over. This PR enhances the
`invokeai-migrate-models-to-db` script so that any customized
configuration data from `invokeai.yaml` replaces the original probed
values. This script only needs to be run once, but it does not hurt to
run it additional times. In the near future, I'm going to register this
module with psychedelicious's sqlite migration system so that the update
happens automatically during database migration.
2. The SQL-based model config record system stores a JSON version of the
config, as well as several fields that are broken out into individual
columns for search/indexing purposes. This PR keeps the JSON and the
broken-out fields in sync using the `json_extract()` sqlite3 function to
populate the broken out `base`, `type`, `name`, `path` and `format`
fields in the `model_config` table.
3. Finally, this PR fixes the annoying `invokeai-web` shutdown message:
`TypeError: ModelInstallService.stop() takes 1 positional argument but 2
were given`
## Related Tickets & Documents
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
If you've run `invokeai-web` at any time since PR #5039, your
`invokeai.db` will have a `model_config` table containing probe
information from all models in the invokeai models directory as well as
those in `autoimport` (if applicable). However, any models present in
`models.yaml` whose paths are outside these directories will not be
present. To add them, and to update the description and other values
from `models.yaml`, run the command `invokeai-migrate-models-to-db`. You
should see the missing models added to the database table with the
correct information.
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [X] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR does three things:
1) It separates out the script that creates the installer zipfile
(`create_installer.sh`) from the script that tags the repository with
the current release version (now called `tag_release.sh`)
2) It adds new targets to Makefile for running the installer script and
tagging.
3) It adds a `help` target that lists the Makefile targets:
```
$ make help
Developer commands:
ruff Run ruff, fixing any safely-fixable errors and formatting
ruff-unsafe Run ruff, fixing all fixable errors and formatting
mypy Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors
mypy-all Run mypy ignoring the config in pyproject.tom but still ignoring missing imports
frontend-build Build the frontend in order to run on localhost:9090
frontend-dev Run the frontend in developer mode on localhost:5173
installer-zip Build the installer .zip file for the current version
tag-release Tag the GitHub repository with the current version (use at release time only!)
```
`help` is also the default target so that the help message will print
out when only `make` is issued.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [X] No: not needed
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Additional tile generation nodes of
CalculateImageTilesEvenSplitInvocation &
CalculateImageTilesMinimumOverlapInvocation
Additional blending method of merge_tiles_with_seam_blending
Updated Node MergeTilesToImageInvocation with seam blending
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Simplifies a couple things:
- Init is more straightforward
- It's clear in the migrator that the connection we are working with is related to the SqliteDatabase
- Simplify init args to path (None means use memory), logger, and verbose
- Add docstrings to SqliteDatabase (it had almost none)
- Update all usages of the class
CONTAINER_UID is used for the user ID within the container, however I noticed the UID was hard coded to 1000 in the Dockerfile chown -R command.
This leaves the default as 1000, but allows it to be overrriden by setting CONTAINER_UID.
- min_overlap removed * restrictions and round_to_8
- min_overlap handles tile size > image size by clipping the num tiles to 1.
- Updated assert test on min_overlap.
On Windows, we must ensure the connection to the database is closed before exiting the tempfile context.
Also, rejiggered the thing to use the file directly.
## What type of PR is this? (check all applicable)
- [X] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This is the next phase of the model manager refactor, as discussed with
@psychedelicious and @RyanJDick. This implements the model installer,
which is responsible for managing model weights on disk and installing
new models.
Currently only installation of local files and directories is supported.
Remote installation will be implemented after the queued download
manager is reviewed and approved.
Please see the documentation located at
[docs/contributing/MODEL_MANAGER.md](8695ad6f59/docs/contributing/MODEL_MANAGER.md (model-installation))
for an explanation of how this module works.
Things that have changed relative to the current implementation.
1. Model importation runs in a background thread. Access to the
installation status is through a ModelInstallJob object returned by the
`import_model()` call. In addition, the installation process generates a
series of `model_install` events on the event bus.
2. `model_install_progress` events are documented, but not currently
issued. These will be issued when background downloading is implemented.
3. The model installer currently runs in parallel to the current model
manager. The frontend continues to use `configs/models.yaml` and ignores
what is in the `model_config` table of `invokeai.db`.
4. When the installer is initialized at app startup time, it
synchronizes its database to the contents of the InvokeAI `models`
directory. The current model manager does this as well, so you will see
two log messages indicating that this directory is being scanned.
## Related Tickets & Documents
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
You can test using the FastAPI swagger pages at
http://localhost:9090/docs. Use the calls listed under
`model_manager_v2`. Be aware that only installation of local models
(indicated by their file or directory path) are currently supported.
## Added/updated tests?
- [X] Yes -- see
`tests/app/services/model_install/test_model_install.py`
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
In other words, build frontend when creating installer.
Changes to `create_installer.sh`
- If `python` is not in `PATH` but `python3` is, alias them (well, via function). This is needed on some machines to run the installer without symlinking to `python3`.
- Make the messages about pushing tags clearer. The script force-pushes, so it's possible to accidentally take destructive action. I'm not sure how to otherwise prevent damage, so I just added a warning.
- Print out `pwd` when prompting about being in the `installer` dir.
- Rebuild the frontend - if there is already a frontend build, first checks if the user wants to rebuild it.
- Checks for existence of `../build` dir before deleting - if the dir doesn't exist, the script errors and exits at this point.
- Format and spell check.
Other changes:
- Ignore `dist/` folder.
- Delete `dist/`.
**Note: you may need to use `git rm --cached invokeai/app/frontend/web/dist/` if git still wants to track `dist/`.**
Calling `inspect.getmembers()` on a pydantic field results in `getattr` being called on all members of the field. Pydantic has some attrs that are marked deprecated.
In our test suite, we do not filter deprecation warnings, so this is surfaced.
Use a context manager to ignore deprecation warnings when calling the function.
In the latest redux, unknown actions are typed as `unknown`. This forces type-safety upon us, requiring us to be more careful about the shape of actions.
In this case, we don't know if the rejection has a payload or what shape it may be in, so we need to do runtime checks. This is implemented with a simple zod schema, but probably the right way to handle this is to have consistent types in our RTK-Query error logic.
There are a few breaking changes, which I've addressed.
The vast majority of changes are related to new handling of `reselect`'s `createSelector` options.
For better or worse, we memoize just about all our selectors using lodash `isEqual` for `resultEqualityCheck`. The upgrade requires we explicitly set the `memoize` option to `lruMemoize` to continue using lodash here.
Doing that required changing our `defaultSelectorOptions`.
Instead of changing that and finding dozens of instances where we weren't using that and instead were defining selector options manually, I've created a pre-configured selector: `createMemoizedSelector`.
This is now used everywhere instead of `createSelector`.
- update all scripts
- update the frontend GH action
- remove yarn-related files
- update ignores
Yarn classic + storybook has some weird module resolution issue due to how it hoists dependencies.
See https://github.com/storybookjs/storybook/issues/22431#issuecomment-1630086092
When I did the `package.json` solution in this thread, it broke vite. Next option is to upgrade to yarn 3 or pnpm. I chose pnpm.
Using default_factory to autogenerate UUIDs doesn't make sense here, and results awkward typescript types.
Remove the default factory and instead manually create a UUID for workflow id. There are only two places where this needs to happen so it's not a big change.
This addresses an edge case where:
1. the workflow references fields that are present on the workflow's nodes, but not on the invocation templates for those nodes and
2. The invocation template for that type does exist
This should be a fairly obscure edge case, but could happen if a user fiddled around with the workflow manually.
I ran into it as a result of two nodes having accidentally mixed up their invocation types, a problem introduced with a wonky merge commit.
This logic is moved into a hook.
This is needed for our context menus to close when the user clicks something in reactflow. It needed to be extended to support menus also.
Disabling these introduces an issue where, if you were on an image with a workflow/metadata, then switch to one without, you can end up on a disabled tab. This could potentially cause a runtime error.
* chore: bump pydantic to 2.5.2
This release fixespydantic/pydantic#8175 and allows us to use `JsonValue`
* fix(ui): exclude public/en.json from prettier config
* fix(workflow_records): fix SQLite workflow insertion to ignore duplicates
* feat(backend): update workflows handling
Update workflows handling for Workflow Library.
**Updated Workflow Storage**
"Embedded Workflows" are workflows associated with images, and are now only stored in the image files. "Library Workflows" are not associated with images, and are stored only in DB.
This works out nicely. We have always saved workflows to files, but recently began saving them to the DB in addition to in image files. When that happened, we stopped reading workflows from files, so all the workflows that only existed in images were inaccessible. With this change, access to those workflows is restored, and no workflows are lost.
**Updated Workflow Handling in Nodes**
Prior to this change, workflows were embedded in images by passing the whole workflow JSON to a special workflow field on a node. In the node's `invoke()` function, the node was able to access this workflow and save it with the image. This (inaccurately) models workflows as a property of an image and is rather awkward technically.
A workflow is now a property of a batch/session queue item. It is available in the InvocationContext and therefore available to all nodes during `invoke()`.
**Database Migrations**
Added a `SQLiteMigrator` class to handle database migrations. Migrations were needed to accomodate the DB-related changes in this PR. See the code for details.
The `images`, `workflows` and `session_queue` tables required migrations for this PR, and are using the new migrator. Other tables/services are still creating tables themselves. A followup PR will adapt them to use the migrator.
**Other/Support Changes**
- Add a `has_workflow` column to `images` table to indicate that the image has an embedded workflow.
- Add handling for retrieving the workflow from an image in python. The image file must be fetched, the workflow extracted, and then sent to client, avoiding needing the browser to parse the image file. With the `has_workflow` column, the UI knows if there is a workflow to be fetched, and only fetches when the user requests to load the workflow.
- Add route to get the workflow from an image
- Add CRUD service/routes for the library workflows
- `workflow_images` table and services removed (no longer needed now that embedded workflows are not in the DB)
* feat(ui): updated workflow handling (WIP)
Clientside updates for the backend workflow changes.
Includes roughed-out workflow library UI.
* feat: revert SQLiteMigrator class
Will pursue this in a separate PR.
* feat(nodes): do not overwrite custom node module names
Use a different, simpler method to detect if a node is custom.
* feat(nodes): restore WithWorkflow as no-op class
This class is deprecated and no longer needed. Set its workflow attr value to None (meaning it is now a no-op), and issue a warning when an invocation subclasses it.
* fix(nodes): fix get_workflow from queue item dict func
* feat(backend): add WorkflowRecordListItemDTO
This is the id, name, description, created at and updated at workflow columns/attrs. Used to display lists of workflowsl
* chore(ui): typegen
* feat(ui): add workflow loading, deleting to workflow library UI
* feat(ui): workflow library pagination button styles
* wip
* feat: workflow library WIP
- Save to library
- Duplicate
- Filter/sort
- UI/queries
* feat: workflow library - system graphs - wip
* feat(backend): sync system workflows to db
* fix: merge conflicts
* feat: simplify default workflows
- Rename "system" -> "default"
- Simplify syncing logic
- Update UI to match
* feat(workflows): update default workflows
- Update TextToImage_SD15
- Add TextToImage_SDXL
- Add README
* feat(ui): refine workflow list UI
* fix(workflow_records): typo
* fix(tests): fix tests
* feat(ui): clean up workflow library hooks
* fix(db): fix mis-ordered db cleanup step
It was happening before pruning queue items - should happen afterwards, else you have to restart the app again to free disk space made available by the pruning.
* feat(ui): tweak reset workflow editor translations
* feat(ui): split out workflow redux state
The `nodes` slice is a rather complicated slice. Removing `workflow` makes it a bit more reasonable.
Also helps to flatten state out a bit.
* docs: update default workflows README
* fix: tidy up unused files, unrelated changes
* fix(backend): revert unrelated service organisational changes
* feat(backend): workflow_records.get_many arg "filter_text" -> "query"
* feat(ui): use custom hook in current image buttons
Already in use elsewhere, forgot to use it here.
* fix(ui): remove commented out property
* fix(ui): fix workflow loading
- Different handling for loading from library vs external
- Fix bug where only nodes and edges loaded
* fix(ui): fix save/save-as workflow naming
* fix(ui): fix circular dependency
* fix(db): fix bug with releasing without lock in db.clean()
* fix(db): remove extraneous lock
* chore: bump ruff
* fix(workflow_records): default `category` to `WorkflowCategory.User`
This allows old workflows to validate when reading them from the db or image files.
* hide workflow library buttons if feature is disabled
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
* add middleware to handle 403 errors
* remove log
* add logic to warn the user if not all requested images could be deleted
* lint
* fix copy
* feat(ui): simplify batchEnqueuedListener error toast logic
* feat(ui): use translations for error messages
* chore(ui): lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
You can only have one pre-commit setup on a repo. Removing husky so it
doesn't interfere with the python pre-commit.
## Related Tickets & Documents
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- Related Issue
https://discord.com/channels/1020123559063990373/1149513625321603162/1181752622684831884
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: minor bug
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
While writing regression tests for the queued downloader I discovered
that when using `InvokeAILogger.get_logger()` to fetch a
previously-created logger it resets that logger's log level to the
default specified in the global config. In other words, this didn't work
as expected:
```
import logging
from invokeai.backend.util.logging import InvokeAILogger
logger1 = InvokeAILogger.get_logger('TestLogger')
logger1.setLevel(logging.DEBUG)
logger2 = InvokeAILogger.get_logger('TestLogger')
assert logger1.level == logging.DEBUG
assert logger2.level == logging.DEBUG
```
This PR fixes the problem and adds a corresponding pytest.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [X] Yes
- [ ] No
## [optional] Are there any post deployment tasks we need to perform?
Adds logic to `DiskLatentsStorage.start()` to empty the latents folder on startup.
Adds start and stop methods to `ForwardCacheLatentsStorage`. This is required for `DiskLatentsStorage.start()` to be called, due to how this particular service breaks the direct DI pattern, wrapping the underlying storage with a cache.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This adds support for at least some of the SDXL embeddings currently
available on Civitai. The embeddings I have tested include:
- https://civitai.com/models/154898/marblingtixl?modelVersionId=173668
- https://civitai.com/models/148131?modelVersionId=167640
-
https://civitai.com/models/123485/hannah-ferguson-or-sdxl-or-comfyui-only-or-embedding?modelVersionId=134674
(said to be "comfyui only")
-
https://civitai.com/models/185938/kendall-jenner-sdxl-embedding?modelVersionId=208785
I am _not entirely sure_ that I have implemented support in the most
elegant way. The issue is that these embeddings have two weight tensors,
`clip_g` and `clip_l`, which correspond to `text_encoder` and
`text_encoder_2` in the main model. When the patcher calls the
ModelPatcher's `apply_ti()` method, I simply check the dimensions of the
incoming text encoder and choose the weights that match the dimensions
of the encoder.
While writing this, I also ran into a possible issue with the Compel
library's `get_pooled_embeddings()` call. It pads the input token list
to the model's max token length and then calls the TI manager to add the
additional tokens from the embedding. However, this ends up making the
input token list longer than the max length, and CLIPTextEncoder crashes
with a tensor size mismatch. I worked around this behavior by making the
TI manager's `expand_textual_inversion_token_ids_if_necessary()` method
remove the excess pads at the end of the token list.
Also note that I have made similar changes to `apply_ti()` in the
ONNXModelPatcher, but haven't tested them yet.
## Related Tickets & Documents
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- Related Issue #
- Closes#4401
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [X] No : We need to create tests for model patching...
## [optional] Are there any post deployment tasks we need to perform?
IndexedDB has a much larger storage limit than LocalStorage, and is widely supported.
Implemented as a custom storage driver for `redux-remember` via `idb-keyval`. `idb-keyval` is a simple wrapper for IndexedDB that allows it to be used easily as a key-value store.
The logic to clear persisted storage has been updated throughout the app.
- Reset init image, control adapter images, and node image fields when their selected image fails to load
- Only do this if the app is connected via socket (this indicates that the image is "really" gone, and there isn't just a transient network issue)
It's possible for image parameters/nodes/states to have reference a deleted image. For example, a resize image node might have an image set on it, and the workflow saved. The workflow contains a hard reference to that image.
The image is deleted and the workflow loaded again later. The deleted image is still in that workflow, but the app doesn't detect that. The result is that the workflow/graph appears to be valid, but will fail on invoke.
This creates a really confusing user experience, where when somebody shares a workflow with an image baked into it, and another person opens it, everything *looks* ok, but the workflow fails with a mysterious error about a missing image.
The problem affects node images, control adapter images and the img2img init image. Resetting the image when it fails to load *and* socket is connected resolves this in a simple way.
The problem also affects canvas images, but we have handle that by displaying an error fallback image, so no change is made there.
Closes#5121
- Parse `anyOf` for enums (present when they are optional)
- Consolidate `FieldTypeParseError` and `UnsupportedFieldTypeError` into `FieldParseError` (there was no difference in handling and it simplifies things a bit)
* add centerpadcrop node
- Allows users to add padding to or crop images from the center
- Also outputs a white mask with the dimensions of the output image for use with outpainting
* add CenterPadCrop to NODES.md
Updates NODES.md with CenterPadCrop entry.
* remove mask & output class
- Remove "ImageMaskOutput" where both image and mask are output
- Remove ability to output mask from node
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Use UTF-8 encoding on reading prompts from files to allow Unicode characters to load correctly.
The following examples currently will not load correctly from a file:
Hello, 世界!
😭🤮💔
Added New Match Histogram node
Updated XYGrid nodes and Prompt Tools nodes
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
This new name more accurately represents that these are fields with a type of `T | T[]`, where the "base" type must be the same on both sides of the union.
Custom nodes have a new attribute `node_pack` indicating the node pack they came from.
- This is displayed in the UI in the icon icon tooltip.
- If a workflow is loaded and a node is unavailable, its node pack will be displayed (if it is known).
- If a workflow is migrated from v1 to v2, and the node is unknown, it falls back to "Unknown". If the missing node pack is installed and the node is updated, the node pack will be updated as expected.
Node authors may now create their own arbitrary/custom field types. Any pydantic model is supported.
Two notes:
1. Your field type's class name must be unique.
Suggest prefixing fields with something related to the node pack as a kind of namespace.
2. Custom field types function as connection-only fields.
For example, if your custom field has string attributes, you will not get a text input for that attribute when you give a node a field with your custom type.
This is the same behaviour as other complex fields that don't have custom UIs in the workflow editor - like, say, a string collection.
feat(ui): fix tooltips for custom types
We need to hold onto the original type of the field so they don't all just show up as "Unknown".
fix(ui): fix ts error with custom fields
feat(ui): custom field types connection validation
In the initial commit, a custom field's original type was added to the *field templates* only as `originalType`. Custom fields' `type` property was `"Custom"`*. This allowed for type safety throughout the UI logic.
*Actually, it was `"Unknown"`, but I changed it to custom for clarity.
Connection validation logic, however, uses the *field instance* of the node/field. Like the templates, *field instances* with custom types have their `type` set to `"Custom"`, but they didn't have an `originalType` property. As a result, all custom fields could be connected to all other custom fields.
To resolve this, we need to add `originalType` to the *field instances*, then switch the validation logic to use this instead of `type`.
This ended up needing a bit of fanagling:
- If we make `originalType` a required property on field instances, existing workflows will break during connection validation, because they won't have this property. We'd need a new layer of logic to migrate the workflows, adding the new `originalType` property.
While this layer is probably needed anyways, typing `originalType` as optional is much simpler. Workflow migration logic can come layer.
(Technically, we could remove all references to field types from the workflow files, and let the templates hold all this information. This feels like a significant change and I'm reluctant to do it now.)
- Because `originalType` is optional, anywhere we care about the type of a field, we need to use it over `type`. So there are a number of `field.originalType ?? field.type` expressions. This is a bit of a gotcha, we'll need to remember this in the future.
- We use `Array.prototype.includes()` often in the workflow editor, e.g. `COLLECTION_TYPES.includes(type)`. In these cases, the const array is of type `FieldType[]`, and `type` is is `FieldType`.
Because we now support custom types, the arg `type` is now widened from `FieldType` to `string`.
This causes a TS error. This behaviour is somewhat controversial (see https://github.com/microsoft/TypeScript/issues/14520). These expressions are now rewritten as `COLLECTION_TYPES.some((t) => t === type)` to satisfy TS. It's logically equivalent.
fix(ui): typo
feat(ui): add CustomCollection and CustomPolymorphic field types
feat(ui): add validation for CustomCollection & CustomPolymorphic types
- Update connection validation for custom types
- Use simple string parsing to determine if a field is a collection or polymorphic type.
- No longer need to keep a list of collection and polymorphic types.
- Added runtime checks in `baseinvocation.py` to ensure no fields are named in such a way that it could mess up the new parsing
chore(ui): remove errant console.log
fix(ui): rename 'nodes.currentConnectionFieldType' -> 'nodes.connectionStartFieldType'
This was confusingly named and kept tripping me up. Renamed to be consistent with the `reactflow` `ConnectionStartParams` type.
fix(ui): fix ts error
feat(nodes): add runtime check for custom field names
"Custom", "CustomCollection" and "CustomPolymorphic" are reserved field names.
chore(ui): add TODO for revising field type names
wip refactor fieldtype structured
wip refactor field types
wip refactor types
wip refactor types
fix node layout
refactor field types
chore: mypy
organisation
organisation
organisation
fix(nodes): fix field orig_required, field_kind and input statuses
feat(nodes): remove broken implementation of default_factory on InputField
Use of this could break connection validation due to the difference in node schemas required fields and invoke() required args.
Removed entirely for now. It wasn't ever actually used by the system, because all graphs always had values provided for fields where default_factory was used.
Also, pydantic is smart enough to not reuse the same object when specifying a default value - it clones the object first. So, the common pattern of `default_factory=list` is extraneous. It can just be `default=[]`.
fix(nodes): fix InputField name validation
workflow validation
validation
chore: ruff
feat(nodes): fix up baseinvocation comments
fix(ui): improve typing & logic of buildFieldInputTemplate
improved error handling in parseFieldType
fix: back compat for deprecated default_factory and UIType
feat(nodes): do not show node packs loaded log if none loaded
chore(ui): typegen
We used the `RealESRGANer` utility class from the repo. It handled model loading and tiled upscaling logic.
Unfortunately, it hasn't been updated in over a year, had no types, and annoyingly printed to console.
I've adapted the class, cleaning it up a bit and removing the bits that are not relevant for us.
Upscaling functionality is identical.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Fixes wrong Q&A Troubleshooting link (original leads to 404)
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
* working on recall height/width
* working on adding resize
* working on feature
* fix(ui): move added translation from dist/ to public/
* fix(ui): use `metadata` as hotkey cb dependency
Using `imageDTO` may result in stale data being used
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* eslint added and new string added
* strings and translation hook added
* more changes made
* missing translation added
* final errors resolve in progress
* all errors resolved
* fix(ui): fix missing import of `t()`
* fix(ui): use plurals for moving images to board translation
* fix(ui): fix typo in translation key
* fix(ui): do not use translation for "invoke ai"
* chore(ui): lint
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: Small obvious fix
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This one-line patch adds support for LCM models such as
`SimianLuo/LCM_Dreamshaper_v7`
## Related Tickets & Documents
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- Closes#4951
## QA Instructions, Screenshots, Recordings
Try installing `SimianLuo/LCM_Dreamshaper_v7` and using with CFG 2.5 and
the LCM scheduler.
<!--
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## Added/updated tests?
- [ ] Yes
- [X] Not needed
This PR adds a link and description to the Remote Image node.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [x] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Adds a description and link to a new community node
## Related Tickets & Documents
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- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : This is only a documentation change
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: community nodes already use these import paths
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
The example custom node code in the docs uses old (?) import paths for
invokeai modules. These paths cause the module to fail to load. This PR
updates them.
## QA Instructions, Screenshots, Recordings
- [x] verified that example code is loaded successfully when copied to
custom nodes directory
- [x] verified that custom node works as expected in workflows
## Added/updated tests?
- [ ] Yes
- [x] No : documentation update
## What type of PR is this? (check all applicable)
3.4.0post3
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
N/A
## Description
3.4.0post2 release - mainly fixes duplicate LoRA patching
* first string only to test
* more strings changed
* almost half strings added in json file
* more strings added
* more changes
* few strings and t function changed
* resolved
* errors resolved
* chore(ui): fmt en.json
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
## What type of PR is this? (check all applicable)
3.4 Release Updates
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
## Related Tickets & Documents
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Resolves two bugs introduced in #5106:
1. Linear UI images sometimes didn't make it to the gallery.
This was a race condition. The VAE decode nodes were handled by the
socketInvocationComplete listener. At that moment, the image was marked
as intermediate. Immediately after this node was handled, a
LinearUIOutputInvocation, introduced in #5106, was handled by
socketInvocationComplete. This node internally sets changed the image to
not intermediate.
During the handling of that socketInvocationComplete, RTK Query would
sometimes use its cache instead of retrieving the image DTO again. The
result is that the UI never got the message that the image was not
intermediate, so it wasn't added to the gallery.
This is resolved by refactoring the socketInvocationComplete listener.
We now skip the gallery processing for linear UI events, except for the
LinearUIOutputInvocation. Images now always make it to the gallery, and
network requests to get image DTOs are substantially reduced.
2. Canvas temp images always went into the gallery
The LinearUIOutputInvocation was always setting its image's
is_intermediate to false. This included all canvas images and resulted
in all canvas temp images going to gallery.
This is resolved by making LinearUIOutputInvocation set is_intermediate
based on `self.is_intermediate`. The behaviour now more or less
mirroring the behaviour of is_intermediate on other image-outputting
nodes, except it doesn't save the image again - only changes it.
One extra minor change - LinearUIOutputInvocation only changes
is_intermediate if it differs from the image's current setting. Very
minor optimisation.
## Related Tickets & Documents
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below.
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- Related Issue
https://discord.com/channels/1020123559063990373/1149513625321603162/1174721072826945638
## QA Instructions, Screenshots, Recordings
Try to reproduce the issues described int he discord thread:
- Images should always go to the gallery from txt2img and img2img
- Canvas temp images should not go to the gallery unless auto-save is
enabled
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [X] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [X] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Resolves two bugs introduced in #5106:
1. Linear UI images sometimes didn't make it to the gallery.
This was a race condition. The VAE decode nodes were handled by the socketInvocationComplete listener. At that moment, the image was marked as intermediate. Immediately after this node was handled, a LinearUIOutputInvocation, introduced in #5106, was handled by socketInvocationComplete. This node internally sets changed the image to not intermediate.
During the handling of that socketInvocationComplete, RTK Query would sometimes use its cache instead of retrieving the image DTO again. The result is that the UI never got the message that the image was not intermediate, so it wasn't added to the gallery.
This is resolved by refactoring the socketInvocationComplete listener. We now skip the gallery processing for linear UI events, except for the LinearUIOutputInvocation. Images now always make it to the gallery, and network requests to get image DTOs are substantially reduced.
2. Canvas temp images always went into the gallery
The LinearUIOutputInvocation was always setting its image's is_intermediate to false. This included all canvas images and resulted in all canvas temp images going to gallery.
This is resolved by making LinearUIOutputInvocation set is_intermediate based on `self.is_intermediate`. The behaviour now more or less mirroring the behaviour of is_intermediate on other image-outputting nodes, except it doesn't save the image again - only changes it.
One extra minor change - LinearUIOutputInvocation only changes is_intermediate if it differs from the image's current setting. Very minor optimisation.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
[feat: add private node for linear UI image
outputting](4599517c6c)
Add a LinearUIOutputInvocation node to be the new terminal node for
Linear UI graphs. This node is private and hidden from the Workflow
Editor, as it is an implementation detail.
The Linear UI was using the Save Image node for this purpose. It allowed
every linear graph to end a single node type, which handled saving
metadata and board. This substantially reduced the complexity of the
linear graphs.
This caused two related issues:
- Images were saved to disk twice
- Noticeable delay between when an image was decoded and showed up in
the UI
To resolve this, the new LinearUIOutputInvocation node will handle
adding an image to a board if one is provided.
Metadata is no longer provided in this unified node. Instead, the
metadata graph helpers now need to know the node to add metadata to and
provide it to the last node that actually outputs an image. This is a
`l2i` node for txt2img & img2img graphs, and a different
image-outputting node for canvas graphs.
HRF poses another complication, in that it changes the terminal node. To
handle this, a new metadata util is added called
`setMetadataReceivingNode()`. HRF calls this to change the node that
should receive the graph's metadata.
This resolves the duplicate images issue and improves perf without
otherwise changing the user experience.
---
Also fixed an issue with HRF metadata.
## Related Tickets & Documents
<!--
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below.
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- Closes#4688
- Closes#4645
## QA Instructions, Screenshots, Recordings
Generate some images with and without a board selected. Images should
end up in the right board per usual, but a bit quicker. Metadata should
still work.
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
Add a LinearUIOutputInvocation node to be the new terminal node for Linear UI graphs. This node is private and hidden from the Workflow Editor, as it is an implementation detail.
The Linear UI was using the Save Image node for this purpose. It allowed every linear graph to end a single node type, which handled saving metadata and board. This substantially reduced the complexity of the linear graphs.
This caused two related issues:
- Images were saved to disk twice
- Noticeable delay between when an image was decoded and showed up in the UI
To resolve this, the new LinearUIOutputInvocation node will handle adding an image to a board if one is provided.
Metadata is no longer provided in this unified node. Instead, the metadata graph helpers now need to know the node to add metadata to and provide it to the last node that actually outputs an image. This is a `l2i` node for txt2img & img2img graphs, and a different image-outputting node for canvas graphs.
HRF poses another complication, in that it changes the terminal node. To handle this, a new metadata util is added called `setMetadataReceivingNode()`. HRF calls this to change the node that should receive the graph's metadata.
This resolves the duplicate images issue and improves perf without otherwise changing the user experience.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
[fix(nodes): bump version of nodes post-pydantic
v2](5cb3fdb64c)
This was not done, despite new metadata fields being added to many
nodes.
[feat(ui): add update node
functionality](3f6e8e9d6b)
A workflow's nodes may update itself, if its major version matches the
template's major version.
If the major versions do not match, the user will need to delete and
re-add the node (current behaviour).
The update functionality is not automatic (for now). The logic to update
the node is pretty simple, but I want to ensure it works well first
before doing it automatically when a workflow is loaded.
- New `Details` tab on Workflow Inspector, displays node title, type,
version, and notes
- Button to update the node is displayed on the `Details` tab
- Add hook to determine if a node needs an update, may be updated (i.e.
major versions match), and the callback to update the node in state
- Remove the notes modal from the little info icon
- Modularize the node building logic
## Related Tickets & Documents
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below.
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pull
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-->
Probably exist but not sure where.
## QA Instructions, Screenshots, Recordings
Load an old workflow with nodes that need to be updated. Click on each
node that needs updating and click the update button. Workflow should
work.
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
A workflow's nodes may update itself, if its major version matches the template's major version.
If the major versions do not match, the user will need to delete and re-add the node (current behaviour).
The update functionality is not automatic (for now). The logic to update the node is pretty simple, but I want to ensure it works well first before doing it automatically when a workflow is loaded.
- New `Details` tab on Workflow Inspector, displays node title, type, version, and notes
- Button to update the node is displayed on the `Details` tab
- Add hook to determine if a node needs an update, may be updated (i.e. major versions match), and the callback to update the node in state
- Remove the notes modal from the little info icon
- Modularize the node building logic
## Description
pin torch==2.1.0, torchvision=0.16.0
Prevents accidental upgrade to unreleased torch 2.1.1, which breaks
stuff
## Related Tickets & Documents
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below.
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-->
- Related Issue #5065
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: it is trivial
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
After the switch to the "ruff" linter, I noticed that the stylecheck
workflow is still described as "black" in the action logs. This small PR
should fix the issue.
No breaking changes for us.
Pydantic is working on its own faster JSON parser, `jiter`, and 2.5.0 starts bringing this in. See https://github.com/pydantic/jiter
There are a number of other bugfixes and minor changes in this version of pydantic.
The FastAPI update is mostly internal but let's stay up to date.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [X] Refactor
## Have you discussed this change with the InvokeAI team?
- [X] Extensively
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
As discussed with @psychedelicious and @RyanJDick, this is the first
phase of the model manager refactor. In this phase, I've added support
for storing model configuration information the `invokeai.db` SQL3
database. All the code is separate from the original model manager, so
for the time being the frontend is still using the original YAML-based
configuration, so the web app still works.
To keep things clean, I've added a new FastAPI route called
`model_records` which can add, update, retrieve and delete model
records.
The architecture is described in the first section of
`docs/contributing/MODEL_MANAGER.md`.
## QA Instructions, Screenshots, Recordings
There is a pytest for the model sql storage backend in
`tests/backend/model_manager_2/test_model_storage_sql.py`.
To populate `invokeai.db` with models from your current `models.yaml`,
do the following:
1. Stop the running server
2. Back up `invokeai.db`
3. Run `pip install -e .` to install the command used in the next step.
4. Run `invokeai-migrate-models-to-db`
This will iterate through `models.yaml` and create equivalent database
entries in the `model_config` table of `invokeai.db`. Only the models
named in the yaml file will be migrated, so anything that is autoloaded
will be ignored.
Note that in order to get the `model_records` router to be recognized by
the swagger API, I had to rebuild the frontend. Not sure why this was
necessary and would appreciate a pointer on a less radical way to do
this.
## Added/updated tests?
- [X] Yes
- [ ] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because it's required
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No, not necessary
## Description
We use Pytorch ~2.1.0 as a dependency for InvokeAI, but the installer
still installs 2.0.1 first until Invoke AIs dependencies kick in which
causes it to get deleted anyway and replaced with 2.1.0. This is
unnecessary and probably not wanted.
Fixed the dependencies for the installation script to install Pytorch
~2.1.0 to begin with.
P.s. Is there any reason why "torchmetrics==0.11.4" is pinned? What is
the reason for that? Does that change with Pytorch 2.1? It seems to work
since we use it already. It would be nice to know the reason.
Greetings
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Bit of a cleanup.
[chore(ui): delete unused
files](5eaea9dd64)
[feat(ui): add eslint rule
react/jsx-no-bind](3a0ec635c9)
This rule enforces no arrow functions in component props. In practice,
it means all functions passed as component props must be wrapped in
`useCallback()`.
This is a performance optimization to prevent unnecessary rerenders.
The rule is added and all violations have been fixed, whew!
[chore(ui): move useCopyImageToClipboard to
common/hooks/](f2d26a3a3c)
[chore(ui): move MM components & store to
features/](bb52861896)
Somehow they had ended up in `features/ui/tabs` which isn't right
## QA Instructions, Screenshots, Recordings
UI should still work.
It builds successfully, and I tested things out - looks good to me.
Do not use `strict=True` when scaling controlnet conditioning.
When using `guess_mode` (e.g. `more_control` or `more_prompt`), `down_block_res_samples` and `scales` are zipped.
These two objects are of different lengths, so using zip's strict mode raises an error.
In testing, `len(scales) === len(down_block_res_samples) + 1`.
It appears this behaviour is intentional, as the final "extra" item in `scales` is used immediately afterwards.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: This is just housekeeping
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No, not needed
## Description
Update Accelerate to the most recent version. No breaking changes.
Tested for 1 week in productive use now.
## Related Tickets & Documents
<!--
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [x] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
This PR introduces [`ruff`](https://github.com/astral-sh/ruff) as the
only linter and formatter needed for the project. It is really fast.
Like, alarmingly fast.
It is a drop-in replacement for flake8, isort, black, and much more.
I've configured it similarly to our existing config.
Note: we had enabled a number of flake8 plugins but didn't have the
packages themselves installed, so they did nothing. Ruff used the
existing config, and found a good number of changes needed to adhere to
those flake8 plugins. I've resolved all violations.
### Code changes
- many
[flake8-comprehensions](https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4)
violations, almost all auto-fixed
- a good handful of
[flake8-bugbear](https://docs.astral.sh/ruff/rules/#flake8-bugbear-b)
violations
- handful of
[pycodestyle](https://docs.astral.sh/ruff/rules/#pycodestyle-e-w)
violations
- some formatting
### Developer Experience
[Ruff integrates with most
editors](https://docs.astral.sh/ruff/integrations/):
- Official VSCode extension
- `ruff-lsp` python package allows it to integrate with any LSP-capable
editor (vim, emacs, etc)
- Can be configured as an external tool in PyCharm
### Github Actions
I've updated the `style-checks` action to use ruff, and deleted the
`pyflakes` action.
## Related Tickets & Documents
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below.
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- Closes#5066
## QA Instructions, Screenshots, Recordings
Have a poke around, and run the app. There were some logic changes but
it was all pretty straightforward.
~~Not sure how to best test the changed github action.~~ Looks like it
just used the action from this PR, that's kinda unexpected but OK.
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
This rule enforces no arrow functions in component props. In practice, it means all functions passed as component props must be wrapped in `useCallback()`.
This is a performance optimization to prevent unnecessary rerenders.
The rule is added and all violations have been fixed, whew!
* adding VAE recall when using all parameters
* adding VAE to the RecallParameters tab in ImageMetadataActions
* checking for nil vae and casting to null if undefined
* adding default VAE to recall actions list if VAE is nullish
* fix(ui): use `lodash-es` for tree-shakeable imports
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* working
* added selector for method
* refactoring graph
* added ersgan method
* fixing yarn build
* add tooltips
* a conjuction
* rephrase
* removed manual sliders, set HRF to calculate dimensions automatically to match 512^2 pixels
* working
* working
* working
* fixed tooltip
* add hrf to use all parameters
* adding hrf method to parameters
* working on parameter recall
* working on parameter recall
* cleaning
* fix(ui): fix unnecessary casts in addHrfToGraph
* chore(ui): use camelCase in addHrfToGraph
* fix(ui): do not add HRF metadata unless HRF is added to graph
* fix(ui): remove unused imports in addHrfToGraph
* feat(ui): do not hide HRF params when disabled, only disable them
* fix(ui): remove unused vars in addHrfToGraph
* feat(ui): default HRF str to 0.35, method ESRGAN
* fix(ui): use isValidBoolean to check hrfEnabled param
* fix(nodes): update CoreMetadataInvocation fields for HRF
* feat(ui): set hrf strength default to 0.45
* fix(ui): set default hrf strength in configSlice
* feat(ui): use translations for HRF features
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes, with @blessedcoolant
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
This PR updates Transformers to the most recent version and fixes the
value `pad_to_multiple_of` for `text_encoder.resize_token_embeddings`
which was introduced with
https://github.com/huggingface/transformers/pull/25088 in Transformers
4.32.0.
According to the [Nvidia
Documentation](https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc),
`Performance is better when equivalent matrix dimensions M, N, and K are
aligned to multiples of 8 bytes (or 64 bytes on A100) for FP16`
This fixes the following error that was popping up before every
invocation starting with Transformers 4.32.0
`You are resizing the embedding layer without providing a
pad_to_multiple_of parameter. This means that the new embedding
dimension will be None. This might induce some performance reduction as
Tensor Cores will not be available. For more details about this, or help
on choosing the correct value for resizing, refer to this guide:
https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc`
This is my first "real" fix PR, so I hope this is fine. Please inform me
if there is anything wrong with this. I am glad to help.
Have a nice day and thank you!
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
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- Related Issue:
https://github.com/huggingface/transformers/issues/26303
- Related Discord discussion:
https://discord.com/channels/1020123559063990373/1154152783579197571
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
We have a number of shared classes, objects, and functions that are used in multiple places. This causes circular import issues.
This commit creates a new `app/shared/` module to hold these shared classes, objects, and functions.
Initially, only `FreeUConfig` and `FieldDescriptions` are moved here. This resolves a circular import issue with custom nodes.
Other shared classes, objects, and functions will be moved here in future commits.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes: @psychedelicious told me to do this :)
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
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-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
I'm not sure if it's correct way of handling things, but correcting this
string to '==0.0.20' fixes xformers install for me - and maybe for
others it will too. Sorry for absolutely incorrect PR.
Please see [this
thread](https://github.com/facebookresearch/xformers/issues/740), this
is the issue I had (trying to install InvokeAI with
Automatic/Manual/StableMatrix way).
With ~=0.0.19 (0.0.22):
```
(InvokeAI) pip install torch torchvision xformers~=0.0.19
Collecting torch
Obtaining dependency information for torch from edce54779f/torch-2.1.0-cp311-cp311-win_amd64.whl.metadata
Using cached torch-2.1.0-cp311-cp311-win_amd64.whl.metadata (25 kB)
Collecting torchvision
Obtaining dependency information for torchvision from ab6f42af83/torchvision-0.16.0-cp311-cp311-win_amd64.whl.metadata
Using cached torchvision-0.16.0-cp311-cp311-win_amd64.whl.metadata (6.6 kB)
Collecting xformers
Using cached xformers-0.0.22.post3.tar.gz (3.9 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [20 lines of output]
Traceback (most recent call last):
File "C:\Users\Drun\invokeai\.venv\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
main()
File "C:\Users\Drun\invokeai\.venv\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Drun\invokeai\.venv\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Drun\AppData\Local\Temp\pip-build-env-rmhvraqj\overlay\Lib\site-packages\setuptools\build_meta.py", line 355, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=['wheel'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Drun\AppData\Local\Temp\pip-build-env-rmhvraqj\overlay\Lib\site-packages\setuptools\build_meta.py", line 325, in _get_build_requires
self.run_setup()
File "C:\Users\Drun\AppData\Local\Temp\pip-build-env-rmhvraqj\overlay\Lib\site-packages\setuptools\build_meta.py", line 507, in run_setup
super(_BuildMetaLegacyBackend, self).run_setup(setup_script=setup_script)
File "C:\Users\Drun\AppData\Local\Temp\pip-build-env-rmhvraqj\overlay\Lib\site-packages\setuptools\build_meta.py", line 341, in run_setup
exec(code, locals())
File "<string>", line 23, in <module>
ModuleNotFoundError: No module named 'torch'
```
With 0.0.20:
```
(InvokeAI) pip install torch torchvision xformers==0.0.20
Collecting torch
Obtaining dependency information for torch from edce54779f/torch-2.1.0-cp311-cp311-win_amd64.whl.metadata
Using cached torch-2.1.0-cp311-cp311-win_amd64.whl.metadata (25 kB)
Collecting torchvision
Obtaining dependency information for torchvision from ab6f42af83/torchvision-0.16.0-cp311-cp311-win_amd64.whl.metadata
Using cached torchvision-0.16.0-cp311-cp311-win_amd64.whl.metadata (6.6 kB)
Collecting xformers==0.0.20
Obtaining dependency information for xformers==0.0.20 from d4a42f582a/xformers-0.0.20-cp311-cp311-win_amd64.whl.metadata
Using cached xformers-0.0.20-cp311-cp311-win_amd64.whl.metadata (1.1 kB)
Collecting numpy (from xformers==0.0.20)
Obtaining dependency information for numpy from 3f826c6d15/numpy-1.26.0-cp311-cp311-win_amd64.whl.metadata
Using cached numpy-1.26.0-cp311-cp311-win_amd64.whl.metadata (61 kB)
Collecting pyre-extensions==0.0.29 (from xformers==0.0.20)
Using cached pyre_extensions-0.0.29-py3-none-any.whl (12 kB)
Collecting torch
Using cached torch-2.0.1-cp311-cp311-win_amd64.whl (172.3 MB)
Collecting filelock (from torch)
Obtaining dependency information for filelock from 97afbafd9d/filelock-3.12.4-py3-none-any.whl.metadata
Using cached filelock-3.12.4-py3-none-any.whl.metadata (2.8 kB)
Requirement already satisfied: typing-extensions in c:\users\drun\invokeai\.venv\lib\site-packages (from torch) (4.8.0)
Requirement already satisfied: sympy in c:\users\drun\invokeai\.venv\lib\site-packages (from torch) (1.12)
Collecting networkx (from torch)
Using cached networkx-3.1-py3-none-any.whl (2.1 MB)
Collecting jinja2 (from torch)
Using cached Jinja2-3.1.2-py3-none-any.whl (133 kB)
Collecting typing-inspect (from pyre-extensions==0.0.29->xformers==0.0.20)
Obtaining dependency information for typing-inspect from 107a22063b/typing_inspect-0.9.0-py3-none-any.whl.metadata
Using cached typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)
Collecting requests (from torchvision)
Obtaining dependency information for requests from 0e2d847013/requests-2.31.0-py3-none-any.whl.metadata
Using cached requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)
INFO: pip is looking at multiple versions of torchvision to determine which version is compatible with other requirements. This could take a while.
Collecting torchvision
Using cached torchvision-0.15.2-cp311-cp311-win_amd64.whl (1.2 MB)
Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)
Obtaining dependency information for pillow!=8.3.*,>=5.3.0 from debe992677/Pillow-10.0.1-cp311-cp311-win_amd64.whl.metadata
Using cached Pillow-10.0.1-cp311-cp311-win_amd64.whl.metadata (9.6 kB)
Collecting MarkupSafe>=2.0 (from jinja2->torch)
Obtaining dependency information for MarkupSafe>=2.0 from 08b85bc194/MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl.metadata
Using cached MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl.metadata (3.1 kB)
Collecting charset-normalizer<4,>=2 (from requests->torchvision)
Obtaining dependency information for charset-normalizer<4,>=2 from 50028bbb26/charset_normalizer-3.3.0-cp311-cp311-win_amd64.whl.metadata
Using cached charset_normalizer-3.3.0-cp311-cp311-win_amd64.whl.metadata (33 kB)
Collecting idna<4,>=2.5 (from requests->torchvision)
Using cached idna-3.4-py3-none-any.whl (61 kB)
Collecting urllib3<3,>=1.21.1 (from requests->torchvision)
Obtaining dependency information for urllib3<3,>=1.21.1 from 9957270221/urllib3-2.0.6-py3-none-any.whl.metadata
Using cached urllib3-2.0.6-py3-none-any.whl.metadata (6.6 kB)
Collecting certifi>=2017.4.17 (from requests->torchvision)
Obtaining dependency information for certifi>=2017.4.17 from 2234eab223/certifi-2023.7.22-py3-none-any.whl.metadata
Using cached certifi-2023.7.22-py3-none-any.whl.metadata (2.2 kB)
Requirement already satisfied: mpmath>=0.19 in c:\users\drun\invokeai\.venv\lib\site-packages (from sympy->torch) (1.3.0)
Collecting mypy-extensions>=0.3.0 (from typing-inspect->pyre-extensions==0.0.29->xformers==0.0.20)
Using cached mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)
Using cached xformers-0.0.20-cp311-cp311-win_amd64.whl (97.6 MB)
Using cached Pillow-10.0.1-cp311-cp311-win_amd64.whl (2.5 MB)
Using cached filelock-3.12.4-py3-none-any.whl (11 kB)
Using cached numpy-1.26.0-cp311-cp311-win_amd64.whl (15.8 MB)
Using cached requests-2.31.0-py3-none-any.whl (62 kB)
Using cached certifi-2023.7.22-py3-none-any.whl (158 kB)
Using cached charset_normalizer-3.3.0-cp311-cp311-win_amd64.whl (97 kB)
Using cached MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl (17 kB)
Using cached urllib3-2.0.6-py3-none-any.whl (123 kB)
Using cached typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)
Installing collected packages: urllib3, pillow, numpy, networkx, mypy-extensions, MarkupSafe, idna, filelock, charset-normalizer, certifi, typing-inspect, requests, jinja2, torch, pyre-extensions, xformers, torchvision
Successfully installed MarkupSafe-2.1.3 certifi-2023.7.22 charset-normalizer-3.3.0 filelock-3.12.4 idna-3.4 jinja2-3.1.2 mypy-extensions-1.0.0 networkx-3.1 numpy-1.26.0 pillow-10.0.1 pyre-extensions-0.0.29 requests-2.31.0 torch-2.0.1 torchvision-0.15.2 typing-inspect-0.9.0 urllib3-2.0.6 xformers-0.0.20
```
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: I'm no-brainer. It fixed issue for me, so I did PR.
Who knows?
## Technical details:
Windows 11, Standalone clean and freshly-installed Python 3.11
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
Removing LowRA from the initial models as it's been deleted from
CivitAI.
## Related Tickets & Documents
https://discord.com/channels/1020123559063990373/1168415065205112872
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Improve LoRA patching speed with the following changes:
- Calculate LoRA layer weights on the same device as the target model.
Prior to this change, weights were always calculated on the CPU. If the
target model is on the GPU, this significantly improves performance.
- Move models to their target devices _before_ applying LoRA patches.
- Improve the ordering of Tensor copy / cast operations.
## QA Instructions, Screenshots, Recordings
Tests:
- [x] Tested with a CUDA GPU, saw savings of ~10secs with 1 LoRA applied
to an SDXL model.
- [x] No regression in CPU-only environment
- [ ] No regression (and possible improvement?) on Mac with MPS.
- [x] Weights get restored correctly after using a LoRA
- [x] Stacking multiple LoRAs
Please hammer away with a variety of LoRAs in case there is some edge
case that I've missed.
## Added/updated tests?
- [x] Yes (Added some minimal unit tests. Definitely would benefit from
more, but it's a step in the right direction.)
- [ ] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR gives the user the option of upgrading to the latest PRE-RELEASE
in addition to the default of updating to the latest release. This will
allow users to conveniently try out the latest pre-release for a while
and then back out to the official release if it doesn't work for them.
Added Average Images node
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [X] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Added a new community node that averages input images.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR prevents the invokeai update script from offering prereleases.
Currently translated at 37.7% (460 of 1217 strings)
translationBot(ui): update translation (German)
Currently translated at 36.4% (444 of 1217 strings)
translationBot(ui): update translation (German)
Currently translated at 36.0% (439 of 1217 strings)
Co-authored-by: Alexander Eichhorn <pfannkuchensack@einfach-doof.de>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI
Currently translated at 37.7% (460 of 1217 strings)
translationBot(ui): update translation (German)
Currently translated at 36.4% (444 of 1217 strings)
translationBot(ui): update translation (German)
Currently translated at 36.4% (443 of 1217 strings)
translationBot(ui): update translation (German)
Currently translated at 36.0% (439 of 1217 strings)
translationBot(ui): update translation (German)
Currently translated at 35.5% (433 of 1217 strings)
Co-authored-by: Fabian Bahl <fabian98@bahl-netz.de>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI
Currently translated at 36.0% (439 of 1217 strings)
translationBot(ui): update translation (German)
Currently translated at 35.5% (433 of 1217 strings)
Co-authored-by: Jaulustus <jaulustus@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI
Currently translated at 56.1% (683 of 1217 strings)
translationBot(ui): update translation (Japanese)
Currently translated at 40.3% (491 of 1217 strings)
Co-authored-by: Gohsuke Shimada <ghoskay@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ja/
Translation: InvokeAI/Web UI
Update to Load Video Frame node to reflect changes made in link
locations... a.k.a. fixing broken links.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x ] Documentation Update
- [x ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x ] No, because: Its just a doc change to fix links I made for
resources that the page depends on from my github.
## Have you updated all relevant documentation?
- [? ] Yes
- [ ] No
## Description
load vid frame community node layout and link change.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because n/a
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
The introduction of `BaseModelType.Any` broke the code in the merge
script which relied on sd-1 coming first in the BaseModelType enum. This
assumption has been removed and the code should be less brittle now.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Fix textual inversion training script crash caused by reorg of services.
## Related Tickets & Documents
- closes#4975
<!--
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below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR allows users to install checkpoint (safetensors) versions of
controlnet models. The models will be converted into diffusers format
and cached on the fly.
This only works for sd-1 and sd-2 controlnets, as I was unable to find
controlnet sdxl checkpoint models or their corresponding .yaml config
files.
After updating, please run `invokeai-configure --yes --default-only` to
install the missing config files. Users should be instructed to select
option [7] from the launcher "Re-run the configure script to fix a
broken install or to complete a major upgrade".
## Related Tickets & Documents
User request at
https://discord.com/channels/1020123559063990373/1160318627631870092/1160318627631870092
<!--
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below.
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pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #4743
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
See above for instructions on updating the config files after checking
out the PR.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
[fix(nodes): fix missing generation
modes](8615d53e65)
Lax typing on the metadata util functions allowed a typing issue to slip
through. Fixed the lax typing, updated core metadata node.
## Related Tickets & Documents
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- Related Issue #
- Closes#4959 (thanks @coder543)
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
fix(nodes): explicitly include custom nodes files
setuptools ignores markdown files - explicitly include all files in
`"invokeai.app.invocations"` to ensure all custom node files are
included
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [x] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
- updates the Docker image with ubuntu23.04 base, python3.11
- use the newer pytorch wheel with cuda12.1 support
- corrects `docker compose` CLI in shell script wrappers and docs
- update / overhaul Docker docs
- clean up obsolete lines in `.gitignore`
## QA Instructions, Screenshots, Recordings
Follow the documentation changes, or simply:
```bash
cd docker
cp .env.sample .env
# Set your INVOKEAI_ROOT in .env
docker compose up
```
## Added/updated tests?
- [ ] Yes
- [x] No : N/A
Custom nodes may be places in `$INVOKEAI_ROOT/nodes/` (configurable with `custom_nodes_dir` option).
On app startup, an `__init__.py` is copied into the custom nodes dir, which recursively loads all python files in the directory as modules (files starting with `_` are ignored). The custom nodes dir is now a python module itself.
When we `from invocations import *` to load init all invocations, we load the custom nodes dir, registering all custom nodes.
Also added config options for metadata and workflow debounce times (`metadataFetchDebounce` & `workflowFetchDebounce`).
Falls back to 0 if not provided.
In OSS, because we have no major latency concerns, the debounce is 0. But in other environments, it may be desirable to set this to something like 300ms.
- Refactor how metadata is handled to support a user-defined metadata in graphs
- Update workflow embed handling
- Update UI to work with these changes
- Update tests to support metadata/workflow changes
This fixes a weird issue where the list images method needed to handle `None` for its `limit` and `offset` arguments, in order to get a count of all intermediates.
On our local installs this will be a very minor change. For those running on remote servers, load times should be slightly improved.
It's a small change but I think correct.
This should prevent `index.html` from *ever* being cached, so UIs will never be out of date.
Minor organisation to accomodate this.
Deleting old unused files from the early days
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
This PR adds the ability to pass multiple images to a single IP-Adapter
(note the difference from using _multiple IP-Adapters at once_, which is
already supported.). The image embeddings are combined in the IP-Adapter
attention layers. This is the same strategy for combining multiple
images as used in Insta-LoRA workflows
(https://civitai.com/articles/2345).
This PR only adds multi-image support in the backend and the node
editor. The Linear UI still needs to be updated.
## QA Instructions, Screenshots, Recordings
I have manually tested the following via the workflow editor:
- Multiple images with a single IP-Adapter
- Multiple images per IP-Adapter, and multiple IP-Adapters
- Both standard and sequential conditioning
- IP-Adapters still work in the Linear UI.
Please hammer at this feature some more with manual testing.
## Added/updated tests?
- [x] Yes
- [ ] No
I updated the existing IP-Adapter smoke test, but it provides pretty
limited coverage of this feature. This feature would probably be best
tested by an end-to-end workflow test, which is not currently supported.
(I'm hoping to put some effort into workflow-level testing soon.)
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
fix(ui): use pidi processor for sketch control adapters
Also, the PREVIOUS commit (@8d3885d, which was already pushed to github repo) was wrongly commented, but too late to fix without a force push or other mucking that I'm reluctant to do. That commit is actually the one that has all the changes to diffusers_pipeline.py to use additional arg down_intrablock_additional_residuals (introduced in diffusers PR https://github.com/huggingface/diffusers/pull/5362) to detangle T2I-Adapter from ControlNet inputs to main UNet.
Upgrade pydantic and fastapi to latest.
- pydantic~=2.4.2
- fastapi~=103.2
- fastapi-events~=0.9.1
**Big Changes**
There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes.
**Invocations**
The biggest change relates to invocation creation, instantiation and validation.
Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie.
Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`.
With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation.
This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method.
In the end, this implementation is cleaner.
**Invocation Fields**
In pydantic v2, you can no longer directly add or remove fields from a model.
Previously, we did this to add the `type` field to invocations.
**Invocation Decorators**
With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper.
A similar technique is used for `invocation_output()`.
**Minor Changes**
There are a number of minor changes around the pydantic v2 models API.
**Protected `model_` Namespace**
All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_".
Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple.
```py
class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
```
**Model Serialization**
Pydantic models no longer have `Model.dict()` or `Model.json()`.
Instead, we use `Model.model_dump()` or `Model.model_dump_json()`.
**Model Deserialization**
Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions.
Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model.
```py
adapter_graph = TypeAdapter(Graph)
deserialized_graph_from_json = adapter_graph.validate_json(graph_json)
deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict)
```
**Field Customisation**
Pydantic `Field`s no longer accept arbitrary args.
Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field.
**Schema Customisation**
FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec.
This necessitates two changes:
- Our schema customization logic has been revised
- Schema parsing to build node templates has been revised
The specific aren't important, but this does present additional surface area for bugs.
**Performance Improvements**
Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node.
I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
There's a bug in chrome that screws with headers on fetch requests and 307 responses. This causes images to fail to copy in the commercial environment.
This change attempts to get around this by copying images in a different way (similar to how the canvas works). When the user requests a copy we:
- create an `<img />` element
- set `crossOrigin` if needed
- add an onload handler:
- create a canvas element
- draw image onto it
- export canvas to blob
This is wrapped in a promise which resolves to the blob, which can then be copied to clipboard.
---
A customized version of Konva's `useImage` hook is also included, which returns the image blob in addition to the `<img />` element. Unfortunately, this hook is not suitable for use across the app, because it does all the image fetching up front, regardless of whether we actually want to copy the image.
In other words, we'd have to fetch the whole image file even if the user is just skipping through image metadata, in order to have the blob to copy. The callback approach means we only fetch the image when the user clicks copy. The hook is thus currently unused.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Fix for breaking change in `python-socketio` 5.10.0 in which
`enter_room` and `leave_room` were made coroutines.
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
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automatically close the issue.
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- Closes#4899
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
fix(ui): fix control adapter translation string
Missed this during a previous change
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
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Reported by @Harvester62 :
https://discord.com/channels/1020123559063990373/1054129386447716433/1162018775437148160
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR strips leading and trailing whitespace from URLs that are
entered into either the Web Model Manager import field, or using the
TUI.
## Related Tickets & Documents
Closes#4536
## QA Instructions, Screenshots, Recordings
Try to import a URL with leading or trailing whitespace. Should not work
in current main. This PR should fix it.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Mac users have a recurring issue in which a `.DS_Store` directory is
created in their `models` hierarchy, causing the new model scanner to
freak out. This PR skips over any paths that begin with a dot. I haven't
tested it on a Macintosh, so I'm not 100% certain it will do the trick.
## Related Tickets & Documents
- Related Issue #4815
## QA Instructions, Screenshots, Recordings
Someone with a Mac please try to reproduce the `.DS_Store` crash and
then see if applying this PR addresses the issue.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
This was in the original fix in #4829 but I must have removed it
accidentally.
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
pull
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- Related Issue #
- Closes#4889
## QA Instructions, Screenshots, Recordings
- Start from a fresh canvas session (may need to let a generation finish
or reset web UI if yours is locked)
- Invoke/add to queue
- Immediately cancel current, clear queue, or clear batch (can do this
from the queue tab)
- Canvas should return to normal state
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
Facetools nodes were cutting off faces that extended beyond chunk boundaries in some cases. All faces found are considered and are coalesced rather than pruned, meaning that you should not see half a face any more.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
[fix(nodes,ui): optional
metadata](78b8cfede3)
- Make all metadata items optional. This will reduce errors related to
metadata not being provided when we update the backend but old queue
items still exist
- Fix a bug in t2i adapter metadata handling where it checked for ip
adapter metadata instaed of t2i adapter metadata
- Fix some metadata fields that were not using `InputField`
- Make all metadata items optional. This will reduce errors related to metadata not being provided when we update the backend but old queue items still exist
- Fix a bug in t2i adapter metadata handling where it checked for ip adapter metadata instaed of t2i adapter metadata
- Fix some metadata fields that were not using `InputField`
Currently translated at 91.4% (1112 of 1216 strings)
translationBot(ui): update translation (Italian)
Currently translated at 90.4% (1100 of 1216 strings)
translationBot(ui): update translation (Italian)
Currently translated at 90.4% (1100 of 1216 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
`mallinfo2` is not available on `glibc` < 2.33.
On these systems, we successfully load the library but get an `AttributeError` on attempting to access `mallinfo2`.
I'm not sure if the old `mallinfo` will work, and not sure how to install it safely to test, so for now we just handle the `AttributeError`.
This means the enhanced memory snapshot logic will be skipped for these systems, which isn't a big deal.
## What type of PR is this? (check all applicable)
- [X] Optimization
-
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR changes the pypi-release workflow so that it will upload to PyPi
whenever a release is initiated from the `main` branch or another branch
beginning with `release/`. Previous support for v2.3 branches has been
removed.
I'm not sure if it's correct way of handling things, but correcting this string to '==0.0.20' fixes xformers install for me - and maybe for others too.
Please see this thread, this is the issue I had (trying to install InvokeAI):
https://github.com/facebookresearch/xformers/issues/740
* added HrfScale type with initial value
* working
* working
* working
* working
* working
* added addHrfToGraph
* continueing to implement this
* working on this
* comments
* working
* made hrf into its own collapse
* working on adding strength slider
* working
* working
* refactoring
* working
* change of this working: 0
* removed onnx support since apparently its not used
* working
* made scale integer
* trying out psycicpebbles idea
* working
* working on this
* working
* added toggle
* comments
* self review
* fixing things
* remove 'any' type
* fixing typing
* changed initial strength value to 3 (large values cause issues)
* set denoising start to be 1 - strength to resemble image to image
* set initial value
* added image to image
* pr1
* pr2
* updating to resolution finding
* working
* working
* working
* working
* working
* working
* working
* working
* working
* use memo
* connect rescale hw to noise
* working
* fixed min bug
* nit
* hides elements conditionally
* style
* feat(ui): add config for HRF, disable if feature disabled or ONNX model in use
* fix(ui): use `useCallback` for HRF toggle
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* #4665 hides value of the corresponding metadata item by click on arrow
* #4787 return recall button back:)
* #4787 optional hide of metadata item, truncation and scrolling
* remove unused import
* #4787 recall parameters as separate tab in panel
* #4787 remove debug code
* fix(ui): undo changes to dist/locales/en.json
This file is autogenerated by our translation system and shouldn't be modified directly
* feat(ui): use scrollbar-enabled component for parameter recall tab
* fix(ui): revert unnecessary changes to DataViewer component
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
v3.3.0 was accidentally released with more changes than intended. This workflows change will allow us release to pypi from a separate branch rather than main.
## What type of PR is this? (check all applicable)
v3.3.0 release
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
The `invokeai-configure` TUI's slider for the RAM cache was not picking
up the current settings in `invokeai.yaml`, leading users to think their
change hadn't taken effect. This is fixed in this PR.
## Related Tickets & Documents
First described here:
https://discord.com/channels/1020123559063990373/1161919551441735711/1162058518417907743
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
A regression in 3.2.0 causes a seemingly nonsensical multiple choice
menu to appear when importing an SD-1 checkpoint model from the
autoimport directory. The menu asks the user to identify which type of
SD-2 model they are trying to import, which makes no sense.
In fact, the menu is popping up because there are now both "epsilon" and
"vprediction" SchedulerPredictionTypes for SD-1 as well as SD-2 models,
and the prober can't determine which prediction type to use. This PR
does two things:
1) rewords the menu as shown below
2) defaults to the most likely choice -- epsilon for v1 models and
vprediction for v2s
Here is the revised multiple-choice menu:
```
Please select the scheduler prediction type of the checkpoint named v1-5-pruned-emaonly.safetensors:
[1] "epsilon" - most v1.5 models and v2 models trained on 512 pixel images
[2] "vprediction" - v2 models trained on 768 pixel images and a few v1.5 models
[3] Accept the best guess; you can fix it in the Web UI later
select [3]>
```
Note that one can also put the appropriate config file into the same
directory as the checkpoint you wish to import. Give it the same name as
the model file, but with the extension `.yaml`. For example
`v1-5-pruned-emaonly.yaml`. The system will notice the yaml file and use
that, suppressing the quiz entirely.
## Related Tickets & Documents
- Closes#4768
- Closes#4827
Refactor services folder/module structure.
**Motivation**
While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward.
**Services**
Services are now in their own folder with a few files:
- `services/{service_name}/__init__.py`: init as needed, mostly empty now
- `services/{service_name}/{service_name}_base.py`: the base class for the service
- `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory`
- `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc
Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename.
There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`.
**Shared**
Things that are used across disparate services are in `services/shared/`:
- `default_graphs.py`: previously in `services/`
- `graphs.py`: previously in `services/`
- `paginatation`: generic pagination models used in a few services
- `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
**Service Dependencies**
Services that depend on other services now access those services via the `Invoker` object. This object is provided to the service as a kwarg to its `start()` method.
Until now, most services did not utilize this feature, and several services required their dependencies to be initialized and passed in on init.
Additionally, _all_ services are now registered as invocation services - including the low-level services. This obviates issues with inter-dependent services we would otherwise experience as we add workflow storage.
**Database Access**
Previously, we were passing in a separate sqlite connection and corresponding lock as args to services in their init. A good amount of posturing was done in each service that uses the db.
These objects, along with the sqlite startup and cleanup logic, is now abstracted into a simple `SqliteDatabase` class. This creates the shared connection and lock objects, enables foreign keys, and provides a `clean()` method to do startup db maintenance.
This is not a service as it's only used by sqlite services.
Currently translated at 98.0% (1186 of 1210 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 98.0% (1179 of 1203 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 97.9% (1175 of 1199 strings)
Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
Currently translated at 92.0% (1104 of 1199 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 92.1% (1105 of 1199 strings)
translationBot(ui): update translation (Chinese (Simplified))
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translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 83.0% (996 of 1199 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 67.5% (810 of 1199 strings)
Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
Currently translated at 85.5% (1026 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 84.7% (1016 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 84.7% (1016 of 1199 strings)
translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
Currently translated at 80.8% (969 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 80.7% (968 of 1199 strings)
translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
Currently translated at 81.3% (959 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.3% (959 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.3% (959 of 1179 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (607 of 607 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (605 of 605 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 65.5% (643 of 981 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (605 of 605 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 81.2% (958 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.2% (958 of 1179 strings)
translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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translationBot(ui): update translation (Italian)
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Currently translated at 59.8% (640 of 1069 strings)
translationBot(ui): update translation (Italian)
Currently translated at 57.2% (612 of 1069 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (607 of 607 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (605 of 605 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (605 of 605 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (602 of 602 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 97.8% (589 of 602 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (603 of 603 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (599 of 599 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (596 of 596 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (595 of 595 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (595 of 595 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (593 of 593 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (592 of 592 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 99.6% (601 of 603 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 99.5% (600 of 603 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (599 of 599 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (596 of 596 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 99.8% (594 of 595 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (593 of 593 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (592 of 592 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (591 of 591 strings)
translationBot(ui): update translation (Italian)
Currently translated at 99.3% (587 of 591 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (586 of 586 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (578 of 578 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (563 of 563 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (559 of 559 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (559 of 559 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (551 of 551 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 99.5% (602 of 605 strings)
translationBot(ui): update translation (Russian)
Currently translated at 99.8% (605 of 606 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (596 of 596 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (595 of 595 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (593 of 593 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (592 of 592 strings)
translationBot(ui): update translation (Russian)
Currently translated at 90.2% (534 of 592 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (543 of 543 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (550 of 550 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (548 of 548 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (546 of 546 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (541 of 541 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (544 of 544 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (543 of 543 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 88.0% (477 of 542 strings)
Co-authored-by: Song, Pengcheng <17528592@qq.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Russian)
Currently translated at 98.8% (536 of 542 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (533 of 533 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (540 of 540 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (538 of 538 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 99.8% (535 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (533 of 533 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (533 of 533 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (591 of 591 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (586 of 586 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (578 of 578 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (563 of 563 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (550 of 550 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (550 of 550 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (548 of 548 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (546 of 546 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (544 of 544 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (543 of 543 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (540 of 540 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (533 of 533 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 99.8% (532 of 533 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (526 of 526 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (519 of 519 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (526 of 526 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (523 of 523 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (519 of 519 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (515 of 515 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (526 of 526 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (523 of 523 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (519 of 519 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (515 of 515 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 98.0% (1186 of 1210 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 98.0% (1179 of 1203 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 97.9% (1175 of 1199 strings)
Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
Currently translated at 92.0% (1104 of 1199 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 92.1% (1105 of 1199 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 83.2% (998 of 1199 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 83.0% (996 of 1199 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 67.5% (810 of 1199 strings)
Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
Currently translated at 87.1% (1054 of 1210 strings)
translationBot(ui): update translation (Italian)
Currently translated at 85.5% (1026 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 84.7% (1016 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 84.7% (1016 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 84.4% (1012 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 84.3% (1011 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 83.5% (1002 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.5% (978 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 80.8% (969 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 80.7% (968 of 1199 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.3% (959 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.3% (959 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.3% (959 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.3% (959 of 1179 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (607 of 607 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (605 of 605 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 65.5% (643 of 981 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (605 of 605 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 81.2% (958 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 81.2% (958 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 76.6% (904 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 76.5% (903 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 71.9% (848 of 1179 strings)
translationBot(ui): update translation (Italian)
Currently translated at 71.7% (845 of 1177 strings)
translationBot(ui): update translation (Italian)
Currently translated at 71.7% (845 of 1177 strings)
translationBot(ui): update translation (Italian)
Currently translated at 67.8% (799 of 1177 strings)
translationBot(ui): update translation (Italian)
Currently translated at 58.5% (689 of 1177 strings)
translationBot(ui): update translation (Italian)
Currently translated at 59.8% (640 of 1069 strings)
translationBot(ui): update translation (Italian)
Currently translated at 57.2% (612 of 1069 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (607 of 607 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (605 of 605 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (605 of 605 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (602 of 602 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 97.8% (589 of 602 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (603 of 603 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (599 of 599 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (596 of 596 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (595 of 595 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (595 of 595 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (593 of 593 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (592 of 592 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 99.6% (601 of 603 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 99.5% (600 of 603 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (599 of 599 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (596 of 596 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 99.8% (594 of 595 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (593 of 593 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (592 of 592 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (591 of 591 strings)
translationBot(ui): update translation (Italian)
Currently translated at 99.3% (587 of 591 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (586 of 586 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (578 of 578 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (563 of 563 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (559 of 559 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (559 of 559 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (551 of 551 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 99.5% (602 of 605 strings)
translationBot(ui): update translation (Russian)
Currently translated at 99.8% (605 of 606 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (596 of 596 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (595 of 595 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (593 of 593 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (592 of 592 strings)
translationBot(ui): update translation (Russian)
Currently translated at 90.2% (534 of 592 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (543 of 543 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (550 of 550 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (548 of 548 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (546 of 546 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (541 of 541 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (544 of 544 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (543 of 543 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 88.0% (477 of 542 strings)
Co-authored-by: Song, Pengcheng <17528592@qq.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Russian)
Currently translated at 98.8% (536 of 542 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (533 of 533 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (540 of 540 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (538 of 538 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 99.8% (535 of 536 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (533 of 533 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (533 of 533 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (591 of 591 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (586 of 586 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (578 of 578 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (563 of 563 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (550 of 550 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (550 of 550 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (548 of 548 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (546 of 546 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (544 of 544 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (543 of 543 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (542 of 542 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (540 of 540 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (536 of 536 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (533 of 533 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 99.8% (532 of 533 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (526 of 526 strings)
translationBot(ui): update translation (Russian)
Currently translated at 100.0% (519 of 519 strings)
Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (526 of 526 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (523 of 523 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (519 of 519 strings)
translationBot(ui): update translation (Italian)
Currently translated at 100.0% (515 of 515 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
Currently translated at 100.0% (526 of 526 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (523 of 523 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (519 of 519 strings)
translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (515 of 515 strings)
Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
feat(ui): add translation strings for clear intermediates
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
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automatically close the issue.
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- Related Issue #
- Closes#4851
## [optional] Are there any post deployment tasks we need to perform?
@Millu this can go into 3.3.0
* UI for bulk downloading boards or groups of images
* placeholder route for bulk downloads that does nothing
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
@Millu pointed out this safetensors PR a few weeks ago, which claimed to
offer a performance benefit:
https://github.com/huggingface/safetensors/pull/362 . It was superseded
by https://github.com/huggingface/safetensors/pull/363 and included in
the latest [safetensors 0.4.0
release](https://github.com/huggingface/safetensors/releases/tag/v0.4.0).
Here are the results from my local performance comparison:
```
Before(0.3.1) / After(0.4.0)
sdxl:main:tokenizer from disk to cpu in 0.46s / 0.46s
sdxl:main:text_encoder from disk to cpu in 2.12s / 2.32s
embroidered_style_v1_sdxl.safetensors:sdxl:lora' from disk to cpu in 0.67s / 0.36s
VoxelXL_v1.safetensors:sdxl:lora' from disk to cpu in 1.64s / 0.60s
ryan_db_sdxl_epoch640.safetensors:sdxl:lora' from disk to cpu in 2.46s / 1.40s
sdxl:main:tokenizer_2 from disk to cpu in 0.37s / 0.39s
sdxl:main:text_encoder_2 from disk to cpu in 3.78s / 4.70s
sdxl:main:unet from disk to cpu in 4.66s / 3.08s
sdxl:main:scheduler from disk to cpu in 0.34s / 0.33s
sdxl:main:vae from disk to cpu in 0.66s / 0.51s
TOTAL GRAPH EXECUTION TIME: 56.489s / 53.416s
```
The benefit was marginal on my system (maybe even within measurement
error), but I figured we might as well pull it.
Add support for FreeU. See:
- https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu
- https://github.com/ChenyangSi/FreeU
Implementation:
- `ModelPatcher.apply_freeu()` handles the enabling freeu (which is very simple with diffusers).
- `FreeUConfig` model added to hold the hyperparameters.
- `freeu_config` added as optional sub-field on `UNetField`.
- `FreeUInvocation` added, works like LoRA - chain it to add the FreeU config to the UNet
- No support for model-dependent presets, this will be a future workflow editor enhancement
Closes#4845
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
This PR optimizes the time to load models from disk.
In my local testing, SDXL text_encoder_2 models saw the greatest
improvement:
- Before change, load time (disk to cpu): 14 secs
- After change, load time (disk to cpu): 4 secs
See the in-code documentation for an explanation of how this speedup is
achieved.
## Related Tickets & Documents
This change was previously proposed on the HF transformers repo, but did
not get any traction:
https://github.com/huggingface/transformers/issues/18505#issue-1330728188
## QA Instructions, Screenshots, Recordings
I don't expect any adverse effects, but the new context manager is
applied while loading **all** models, so it would make sense to exercise
everything.
## Added/updated tests?
- [x] Yes
- [ ] No
The canvas needs to be set to staging mode as soon as a canvas-destined batch is enqueued. If the batch is is fully canceled before an image is generated, we need to remove that batch from the canvas `batchIds` watchlist, else canvas gets stuck in staging mode with no way to exit.
The changes here allow the batch status to be tracked, and if a batch has all its items completed, we can remove it from the `batchIds` watchlist. The `batchIds` watchlist now accurately represents *incomplete* canvas batches, fixing this cause of soft lock.
The UI will always re-fetch queue and batch status on receiving this event, so we may as well jsut include that data in the event and save the extra network roundtrips.
## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] No, because: Non-controversial
## Have you updated all relevant documentation?
- [ ] Yes
- [X] N/A
## Description
This adds a list of T2I adapters to the “starter models” offered by the
TUI installer. None of the models is selected by default; this can be
done easily if requested. The models offered to the user are:
```
TencentARC/t2iadapter_canny_sd15v2
TencentARC/t2iadapter_sketch_sd15v2
TencentARC/t2iadapter_depth_sd15v2
TencentARC/t2iadapter_zoedepth_sd15v1
TencentARC/t2i-adapter-canny-sdxl-1.0
TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
TencentARC/t2i-adapter-lineart-sdxl-1.0
TencentARC/t2i-adapter-sketch-sdxl-1.0
```
## Related Tickets & Documents
PR #4612
## QA Instructions, Screenshots, Recordings
The revised installer has a new IP-ADAPTERS tab that looks like this:
## Added/updated tests?
- [ ] Yes
- [X] No : It would be good to have a suite of model download tests, but
not set up yet.
- Update backend metadata for t2i adapter
- Fix typo in `T2IAdapterInvocation`: `ip_adapter_model` -> `t2i_adapter_model`
- Update linear graphs to use t2i adapter
- Add client metadata recall for t2i adapter
- Fix bug with controlnet metadata recall - processor should be set to 'none' when recalling a control adapter
Control adapters logic/state/ui is now generalized to hold controlnet, ip_adapter and t2i_adapter. In the future, other control adapter types can be added.
TODO:
- Limit IP adapter to 1
- Add T2I adapter to linear graphs
- Fix autoprocess
- T2I metadata saving & recall
- Improve on control adapters UI
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
This PR adds support for slow unit tests that depend on models. It
includes:
- Documentation explaining the handling of fast vs. slow unit tests.
- Utilities to assist with writing tests that depend on models.
- A sample test that loads and runs an IP-Adapter model. This is far
from complete test coverage of IP-Adapter - it's just intended as a
first example of how to write tests with models.
**Suggestion for reviewers**: Start with docs/contributing/TESTS.md
## QA Instructions, Screenshots, Recordings
I've tested it all, but it would make sense for others to try running
both the fast tests and the slow tests.
## Added/updated tests?
- [x] Yes
- [ ] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
This PR adds detailed debug logging to the model cache in order to give
more visibility into the model cache's memory utilization. **This PR
does not make any functional changes to the model cache.**
Every time a model is moved from disk to CPU, or between CPU/CUDA, a log
like this is emitted:
```bash
[2023-10-03 15:17:20,599]::[InvokeAI]::DEBUG --> Moved model '/home/ryan/invokeai/models/.cache/63742ed45b499e55620c402d6df26a20:sdxl:main:unet' from cpu to cuda in 1.23s.
Estimated model size: 4.782 GB.
Process RAM (-4.722): 6.987GB -> 2.265GB
libc mmap allocated (-4.722): 6.030GB -> 1.308GB
libc arena used (-0.061): 0.402GB -> 0.341GB
libc arena free (+0.061): 0.006GB -> 0.067GB
libc total allocated (-4.722): 6.439GB -> 1.717GB
libc total used (-4.783): 6.433GB -> 1.649GB
VRAM (+4.881): 1.538GB -> 6.418GB
```
## Related Tickets & Documents
https://github.com/invoke-ai/InvokeAI/pull/4694 contains related fixes
to some known memory issues.
## QA Instructions, Screenshots, Recordings
Make sure debug logs are enabled and you should see the new logs.
We should test each of the following environments:
- [x] Linux
- [x] Mac OS + MPS
- [x] Windows
## Added/updated tests?
- [x] Yes
- [ ] No
Added unit tests for the new utilities. Test coverage is still low for
the ModelCache, but not worse than before.
* Bump diffusers to 0.21.2.
* Add T2IAdapterInvocation boilerplate.
* Add T2I-Adapter model to model-management.
* (minor) Tidy prepare_control_image(...).
* Add logic to run the T2I-Adapter models at the start of the DenoiseLatentsInvocation.
* Add logic for applying T2I-Adapter weights and accumulating.
* Add T2IAdapter to MODEL_CLASSES map.
* yarn typegen
* Add model probes for T2I-Adapter models.
* Add all of the frontend boilerplate required to use T2I-Adapter in the nodes editor.
* Add T2IAdapterModel.convert_if_required(...).
* Fix errors in T2I-Adapter input image sizing logic.
* Fix bug with handling of multiple T2I-Adapters.
* black / flake8
* Fix typo
* yarn build
* Add num_channels param to prepare_control_image(...).
* Link to upstream diffusers bugfix PR that currently requires a workaround.
* feat: Add Color Map Preprocessor
Needed for the color T2I Adapter
* feat: Add Color Map Preprocessor to Linear UI
* Revert "feat: Add Color Map Preprocessor"
This reverts commit a1119a00bf.
* Revert "feat: Add Color Map Preprocessor to Linear UI"
This reverts commit bd8a9b82d8.
* Fix T2I-Adapter field rendering in workflow editor.
* yarn build, yarn typegen
---------
Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
When the processor has an error and it has a queue item, mark that item failed.
This addresses processor errors resulting in `in_progress` queue items, which create a soft lock of the processor, requiring the user to cancel the `in_progress` item before anything else processes.
Makes graph validation logic more rigorous, validating graphs when they are created as part of a session or batch.
`validate_self()` method added to `Graph` model. It does all the validation that `is_valid()` did, plus a few extras:
- unique `node.id` values across graph
- node ids match their key in `Graph.nodes`
- recursively validate subgraphs
- validate all edges
- validate graph is acyclical
The new method is required because `is_valid()` just returned a boolean. That behaviour is retained, but `validate_self()` now raises appropriate exceptions for validation errors. This are then surfaced to the client.
The function is named `validate_self()` because pydantic reserves `validate()`.
There are two main places where graphs are created - in batches and in sessions.
Field validators are added to each of these for their `graph` fields, which call the new validation logic.
**Closes #4744**
In this issue, a batch is enqueued with an invalid graph. The output field is typed as optional while the input field is required. The field types themselves are not relevant - this change addresses the case where an invalid graph was created.
The mismatched types problem is not noticed until we attempt to invoke the graph, because the graph was never *fully* validated. An error is raised during the call to `graph_execution_state.next()` in `invoker.py`. This function prepares the edges and validates them, raising an exception due to the mismatched types.
This exception is caught by the session processor, but it doesn't handle this situation well - the graph is not marked as having an error and the queue item status is never changed. The queue item is therefore forever `in_progress`, so no new queue items are popped - the app won't do anything until the queue item is canceled manually.
This commit addresses this by preventing invalid graphs from being created in the first place, addressing a substantial number of fail cases.
The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize
Closes#4786
This is fired when the dnd image is moved over the 'none' board. Weren't defaulting to 'none' for the image's board_id, resulting in it being possible to drag a 'none' image onto 'none'.
Selections were not being `uniqBy()`'d, or were `uniqBy()`'d without a proper iteratee. This results in duplicate images in selections in certain situations.
Add correct `uniqBy()` to the reducer to prevent this in the future.
This caused a crapload of network requests any time an image was generated.
The counts are necessary to handle the logic for inserting images into existing image list caches; we have to keep track of the counts.
Replace tag invalidation with manual cache updates in all cases, except the initial request (which is necessary to get the initial image counts).
One subtle change is to make the counts an object instead of a number. This is required for `immer` to handle draft states. This should be raised as a bug with RTK Query, as no error is thrown when attempting to update a primitive immer draft.
The helper function `generate_face_box_mask()` had a bug that prevented larger faces from being detected in some situations. This is resolved, and its dependent nodes (all the FaceTools nodes) have a patch version bump.
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
This PR causes the font "Inter-Regular.ttf", which is needed by the
facetools Face Identifier node, to be installed along with other assets
in the virtual environment. It also fixes the font path resolution logic
in the invocation to work with both package and editable installs.
## Related Tickets & Documents
Closes#4771
## What type of PR is this? (check all applicable)
Release v3.2.0
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
Need to update prompting docs
## Description
3.2.0 release version
## [optional] Are there any post deployment tasks we need to perform?
* feat(ui): max upscale pixels config
Add `maxUpscalePixels: number` to the app config. The number should be the *total* number of pixels eg `maxUpscalePixels: 4096 * 4096`.
If not provided, any size image may be upscaled.
If the config is provided, users will see be advised if their image is too large for either model, or told to switch to an x2 model if it's only too large for x4.
The message is via tooltip in the popover and via toast if the user uses the hotkey to upscale.
* feat(ui): "mayUpscale" -> "isAllowedToUpscale"
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [x] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Grid to Gif is two custom nodes, one that divides a grid image into an
image collection, the other converts an image collection into a animated
gif
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ x ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ x ] No, because:
## Have you updated all relevant documentation?
- [x ] Yes
- [ ] No
cv2 infill node was missing a version in its decorator, resulting in a
red exclamation mark on the node
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: is tiny
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Very tall IP adapter images didn't get fit to the panel. Now they do
* Initial commit of edge drag feature.
* Fixed build warnings
* code cleanup and drag to existing node
* improved isValidConnection check
* fixed build issues, removed cyclic dependency
* edge created nodes now spawn at cursor
* Add Node popover will no longer show when using drag to delete an edge.
* Fixed collection handling, added priority for handles matching name of source handle, removed current image/notes nodes from filtered list
* Fixed not properly clearing startParams when closing the Add Node popover
* fix(ui): do not allow Collect -> Iterate connection
This can be removed when #3956 is resolved
* feat(ui): use existing node validation logic in add-node-on-drop
This logic handles a number of special cases
---------
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* node-FaceTools
* Added more documentation for facetools
* invert FaceMask masking
- FaceMask had face protected and surroundings change by default (face white, else black)
- Change to how FaceOff/others work: the opposite where surroundings protected, face changes by default (face black, else white)
* reflect changed facemask behaviour in docs
* add FaceOff+FaceMask workflows
- Add FaceOff and FaceMask example workflows to docs/workflows
* add FaceMask+FaceOff workflows to exampleworkflows.md
- used invokeai URL paths mimicking other workflow URLs, hopefully they translate when/if merged
* inheriting, typehints, black/isort/flake8
- modified FaceMask and FaceOff output classes to inherit base image, height, width from ImageOutput
- Added type annotations to helper functions, required some reworking of code's stored data
* remove credit header
- Was in my personal/repo copy, don't think it's necessary if merged.
* Optionals & image declaration duplication
- Added Optional[] to optional outputs and types
- removed duplication of image = context.services.images.get_pil_images(self.image.image_name) declaration
- Still need to find a way to deal with mask_pil None typing errors
* face(facetools): fix typing issues, add validation, clean up structure
* feat(facetools): update field descriptions
* Update FaceOff_FaceScale2x.json
- update FaceOff workflow after Bounded Image field removed in place of inheriting Image out field from ImageOutput
* feat(facetools): pass through original image on facemask if invalid face ids requested
* feat(facetools): tidy variable names & fn calls
* feat(facetools): bundle inter font, draw ids with it
Inter is a SIL Open Font license. The license is included and is fully permissive. Inter is the same font the UI and commercial application already uses.
Only the "regular" version is bundled.
* chore(facetools): isort & fix mypy issues
* docs(facetools): update and format docs
---------
Co-authored-by: Millun Atluri <millun.atluri@gmail.com>
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* add control net to useRecallParams
* got recall controlnets working
* fix metadata viewer controlnet
* fix type errors
* fix controlnet metadata viewer
* add ip adapter to metadata
* added ip adapter to recall parameters
* got ip adapter recall working, still need to fix type errors
* fix type issues
* clean up logs
* python formatting
* cleanup
* fix(ui): only store `image_name` as ip adapter image
* fix(ui): use nullish coalescing operator for numbers
Need to use the nullish coalescing operator `??` instead of false-y coalescing operator `||` when the value being check is a number. This prevents unintended coalescing when the value is zero and therefore false-y.
* feat(ui): fall back on default values for ip adapter metadata
* fix(ui): remove unused schema
* feat(ui): re-use existing schemas in metadata schema
* fix(ui): do not disable invocationCache
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
[## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
Very rarely a model lives in the subfolder of a non-pipeline HuggingFace
repo_id. The example I've been working with is
https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster/tree/main,
where the improved monster QR code controlnet model lives in the `v2`
subdirectory.
In order to accommodate installing such files, I have made two changes
to the model installer.
1. At installation/configuration time, if a stanza in
`INITIAL_MODELS.yaml` contains the field `subfolder`, then the model
will be installed from the indicated subfolder. The syntax in this case
is:
```
sd-1/controlnet/qrcode_monster:
repo_id: monster-labs/control_v1p_sd15_qrcode_monster
subfolder: v2
```
2. From within the Web GUI or the installer TUI, if you wish to indicate
that the model resides in a subfolder, you can tack ":_subfoldername_"
to the end of the repo_id. The resulting repo_id will look like:
```
monster-labs/control_v1p_sd15_qrcode_monster:v2
```
The code for introducing these changes is obscure and somewhat hacky.
However, the whole installer code base has been rewritten for the model
manager refactor (#4252 ) and I will reimplement this feature in a more
elegant way in that PR.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
This hook was rerendering any time anything changed. Moved it to a logical component, put its useEffects inside the component. This reduces the effect of the rerenders to just that tiny always-null component.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
The IP-Adapter memory footprint was not being calculated correctly.
I think we could put checks in place to catch this type of error in the
future, but for now I'm just fixing the bug.
## QA Instructions, Screenshots, Recordings
I tested manually in a debugger. There are 3 pathways for calculating
the model size. All were tested:
- From file
- From state_dict
- From model weights
## Added/updated tests?
- [ ] Yes
- [x] No : This would require the ability to run tests that depend on
models. I'm working on this in another branch, but not ready quite yet.
* add control net to useRecallParams
* got recall controlnets working
* fix metadata viewer controlnet
* fix type errors
* fix controlnet metadata viewer
* set control image and use correct processor type and node
* clean up logs
* recall processor using substring
* feat(ui): enable controlNet when recalling one
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
- Current image number & total are displayed
- Left/right wrap around instead of stopping on first/last image
- Disable the left/right/number buttons when showing base layer
- improved translations
- Drag the end of an edge away from its handle to disconnect it
- Drop in empty space to delete the edge
- Drop on valid handle to reconnect it
- Update connection logic slightly to allow edge updates
* feat(ui): add error handling for enqueueBatch route, remove sessions
This re-implements the handling for the session create/invoke errors, but for batches.
Also remove all references to the old sessions routes in the UI.
* feat(ui): improve canvas image error UI
* make canvas error state gray instead of red
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] No - this should go into release notes.
## Description
During installation, the installer will now ask the user whether they
wish to perform a manual or automatic configuration of invokeai. If they
choose automatic (the default), then the install is performed without
running the TUI of the `invokeai-configure` script. Otherwise the
console-based interface is activated as usual.
This script also bumps up the default model RAM cache size to 7.5, which
improves performance on SDXL models.
* Add 'Random Float' node <3
does what it says on the tin :)
* Add random float + random seeded float nodes
altered my random float node as requested by Millu, kept the seeded version as an alternate variant for those that would like to control the randomization seed :)
* Update math.py
* Update math.py
* feat(nodes): standardize fields to match other nodes
---------
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* fix(nodes): do not disable invocation cache delete methods
When the runtime disabled flag is on, do not skip the delete methods. This could lead to a hit on a missing resource.
Do skip them when the cache size is 0, because the user cannot change this (must restart app to change it).
* fix(nodes): do not use double-underscores in cache service
* Thread lock for cache
* Making cache LRU
* Bug fixes
* bugfix
* Switching to one Lock and OrderedDict cache
* Removing unused imports
* Move lock cache instance
* Addressing PR comments
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Martin Kristiansen <martin@modyfi.io>
* add skeleton loading state for queue lit
* hide use cache checkbox if cache is disabled
* undo accidental add
* feat(ui): hide node footer entirely if nothing to show there
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Skeletons are for when we know the number of specific content items that are loading. When the queue is loading, we don't know how many items there are, or how many will load, so the whole list should be replaced with loading state.
The previous behaviour rendered a static number of skeletons. That number would rarely be the right number - the app shouldn't say "I'm loading 7 queue items", then load none, or load 50.
A future enhancement could use the queue item skeleton component and go by the total number of queue items, as reported by the queue status. I tried this but had some layout jankiness, not worth the effort right now.
The queue item skeleton component's styling was updated to support this future enhancement, making it exactly the same size as a queue item (it was a bit smaller before).
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Description
I left a dangling debug statement in a recent merged PR (#4674 ). This
removes it.
Updates my Image & Mask Composition Pack from 4 to 14 nodes, and moves
the Enhance Image node into it.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [X] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
This is an update of my existing community nodes entries.
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Adds 9 more nodes to my Image & Mask Composition pack including Clipseg,
Image Layer Blend, Masked Latent/Noise Blend, Image Dilate/Erode,
Shadows/Highlights/Midtones masks from image, and more.
## Related Tickets & Documents
n/a
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [X] No : out of scope, tested the nodes, will integrate tests with my
own repo in time as is helpful
Adds 9 more of my nodes to the Image & Mask Composition Pack in the community nodes page, and integrates the Enhance Image node into that pack as well (formerly it was its own entry).
Add some instructions about installing the frontend toolchain when doing
a git-based install.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Description
[Update
020_INSTALL_MANUAL.md](73ca8ccdb3)
Add some instructions about installing the frontend toolchain when doing
a git-based install.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
This is actually a platform-specific issue. `madge` is complaining about
a circular dependency on a single file -
`invokeai/frontend/web/src/features/queue/store/nanoStores.ts`. In that
file, we import from the `nanostores` package. Very similar name to the
file itself.
The error only appears on Windows and macOS, I imagine because those
systems both resolve `nanostores` to itself before resolving to the
package.
The solution is simple - rename `nanoStores.ts`. It's now
`queueNanoStore.ts`.
## Related Tickets & Documents
https://discord.com/channels/1020123559063990373/1155434451979993140
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## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
This PR adds support for selecting and installing IP-Adapters at
configure time. The user is offered the four existing InvokeAI IP
Adapters in the UI as shown below. The matching image encoders are
selected and installed behind the scenes. That is, if the user selects
one of the three sd15 adapters, then the SD encoder will be installed.
If they select the sdxl adapter, then the SDXL encoder will be
installed.
Note that the automatic selection of the encoder does not work when the
installer is run in headless mode. I may be able to fix that soon, but
I'm out of time today.
This is actually a platform-specific issue. `madge` is complaining about a circular dependency on a single file - `invokeai/frontend/web/src/features/queue/store/nanoStores.ts`. In that file, we import from the `nanostores` package. Very similar name to the file itself.
The error only appears on Windows and macOS, I imagine because those systems both resolve `nanostores` to itself before resolving to the package.
The solution is simple - rename `nanoStores.ts`. It's now `queueNanoStore.ts`.
## What type of PR is this? (check all applicable)
- [X] Bug Fix
- [ ] Optimizatio
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] Np
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
ip_adapter models live in a folder containing the file
`image_encoder.txt` and a safetensors file. The load-time probe for new
models was detecting the files contained within the folder rather than
the folder itself, and so models.yaml was not getting correctly updated.
This fixes the issue.
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
It turns out that there are a few SD-1 models that use the
`v_prediction` SchedulerPredictionType. Examples here:
https://huggingface.co/zatochu/EasyFluff/tree/main . Previously we only
allowed the user to set the prediction type for sd-2 models. This PR
does three things:
1. Add a new checkpoint configuration file `v1-inference-v.yaml`. This
will install automatically on new installs, but for existing installs
users will need to update and then run `invokeai-configure` to get it.
2. Change the prompt on the web model install page to indicate that some
SD-1 models use the "v_prediction" method
3. Provide backend support for sd-1 models that use the v_prediction
method.
## Related Tickets & Documents
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- Related Issue #
- Closes#4277
## QA Instructions, Screenshots, Recordings
Update, run `invoke-ai-configure --yes --skip-sd --skip-support`, and
then use the web interface to install
https://huggingface.co/zatochu/EasyFluff/resolve/main/EasyFluffV11.2.safetensors
with the prediction type set to "v_prediction." Check that the installed
model uses configuration `v1-inference-v.yaml`.
If "None" is selected from the install menu, check that SD-1 models
default to `v1-inference.yaml` and SD-2 default to
`v2-inference-v.yaml`.
Also try installing a checkpoint at a local path if a like-named config
.yaml file is located next to it in the same directory. This should
override everything else and use the local path .yaml.
## Added/updated tests?
- [ ] Yes
- [X] No
## What type of PR is this? (check all applicable)
- [X] Refactor
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: trivial fix
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
It annoyed me that the class method to get the invokeai logger was
`InvokeAILogger.getLogger()`. We do not use camelCase anywhere else. So
this PR renames the method `get_logger()`.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Pydantic handles the casting so this is always safe.
Also de-duplicate some validation logic code that was needlessly
duplicated.
- Change translations to use arrays of paragraphs instead of a single paragraph.
- Change component to accept a `feature` prop to identify the feature which the popover describes.
- Add optional `wrapperProps`: passed to the wrapper element, allowing more flexibility when using the popover
- Add optional `popoverProps`: passed to the `<Popover />` component, allowing for overriding individual instances of the popover's props
- Move definitions of features and popover settings to `invokeai/frontend/web/src/common/components/IAIInformationalPopover/constants.ts`
- Add some type safety to the `feature` prop
- Edit `POPOVER_DATA` to provide `image`, `href`, `buttonLabel`, and any popover props. The popover props are applied to all instances of the popover for the given feature. Note that the component prop `popoverProps` will override settings here.
- Remove the popover's arrow. Because the popover is wrapping groups of components, sometimes the error ends up pointing to nothing, which looks kinda janky. I've just removed the arrow entirely, but feel free to add it back if you think it looks better.
- Use a `link` variant button with external link icon to better communicate that clicking the button will open a new tab.
- Default the link button label to "Learn More" (if a label is provided, that will be used instead)
- Make default position `top`, but set manually set some to `right` - namely, anything with a dropdown. This prevents the popovers from obscuring or being obscured by the dropdowns.
- Do a bit more restructuring of the Popover component itself, and how it is integrated with other components
- More ref forwarding
- Make the open delay 1s
- Set the popovers to use lazy mounting (eg do not mount until the user opens the thing)
- Update the verbiage for many popover items and add missing dynamic prompts stuff
When the runtime disabled flag is on, do not skip the delete methods. This could lead to a hit on a missing resource.
Do skip them when the cache size is 0, because the user cannot change this (must restart app to change it).
- No longer need to make network request to add image to board after it's finished - removed
- Update linear graphs & upscale graph to save image to the board
- Update autoSwitch logic so when image is generated we still switch to the right board
- Remove the add-to-board node
- Create `BoardField` field type & add it to `save_image` node
- Add UI for `BoardField`
- Tighten up some loose types
- Make `save_image` node, in workflow editor, default to not intermediate
- Patch bump `save_image`
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [ ] Yes
- [X] N/A
## Description
Pedantic was misconfigured and was not picking up the INVOKEAI_ prefix
on environment variables. Therefore, if the system had an unrelated
environment variable such as `version`, this caused pedantic validation
errors.
## Related Tickets & Documents
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- Related Issue #
- Closes#4098
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [X] Yes — regression tests run; new regression test added.
* break out separate functions for preselected images, remove recallAllParameters dep as it causes circular logic with model being set
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
…nd move on
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
- New routes to clear, enable, disable and get the status of the cache
- Status includes hits, misses, size, max size, enabled
- Add client cache queries and mutations, abstracted into hooks
- Add invocation cache status area (next to queue status) w/ buttons
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Fixes failure on SDXL metadata node, introduced by me in #4625
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
[feat(ui): enable control adapters on image
drop](aa4b56baf2)
- Dropping/uploading an image on control adapter enables it (controlnet
& ip adapter)
- The image components are always enabled to allow this
Hide it until #4624 is ready
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
feat(ui): hide clipskip on sdxl; do not add to metadata
Hide it until #4624 is ready
## Related Tickets & Documents
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- Closes#4618
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
fix(ui): add control adapters to canvas coherence pass
## Related Tickets & Documents
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- Closes#4619
- Closes#4589
## QA Instructions, Screenshots, Recordings
I cannot figure out how to get the CLIP Vision model installed but I can
confirm that the graph is correct, because I get a Model Not Found error
that references this model, when invoking with IP adapter enabled..
* Initial commit. Feature works, but code might need some cleanup
* Cleaned up diff
* Made mousePosition a XYPosition again so its nicely typed
* Fixed yarn issues
* Paste now properly takes node width/height into account when pasting
* feat(ui): use react's types in the `onMouseMove` `reactflow` handler
* feat(ui): use refs to access `reactflow`'s DOM elements
* feat(ui): use a ref to store cursor position in nodes
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Polymorphic fields now render the appropriate input component for their base type.
For example, float polymorphics will render the number input box.
You no longer need to specify ui_type to force it to display.
TODO: The UI *may* break if a list is provided as the default value for a polymorphic field.
* Remove fastapi-socketio dependency, doesn't really do much for us and isn't well maintained
* Run python black
* Remove fastapi_socketio import
* Add __app as class variable in case we ever need it later
* Run isort
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
[TAESD - Tiny Autoencoder for Stable
Diffusion](https://github.com/madebyollin/taesd) - is a tiny VAE that
provides significantly better results than my single-multiplication hack
but is still very fast.
The entire TAESD model weights are under 10 MB!
This PR requires diffusers 0.20:
- [x] #4311
## To Do
Test with
- [x] SD 1.x
- [ ] SD 2.x: #4415
- [x] SDXL
## Have you discussed this change with the InvokeAI team?
- See [TAESD Invocation
API](https://discord.com/channels/1020123559063990373/1137857402453119166)
## Have you updated all relevant documentation?
- [ ] No
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
Should be able to import these models:
- [madebyollin/taesd](https://huggingface.co/madebyollin/taesd)
- [madebyollin/taesdxl](https://huggingface.co/madebyollin/taesdxl)
and use them as VAE.
<!--
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## Added/updated tests?
- [x] Some. There are new tests for VaeFolderProbe based on VAE
configurations, but no tests that require the full model weights.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
This is a doc file that was missing from PR #4587 . Since that PR was
already merged. I’m pushing it in now.
## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] No, because it is trivial
## Have you updated all relevant documentation?
- [X] Yes -- added a new page listing all the command-line scripts and
their most useful options.
## Description
InvokeAI version 2.3 had a script called `invokeai-metadata` that
accepted a list of png images and printed out JSON-formatted embedded
metadata. I used to use the script for sorting and tagging images
outside of the InvokeAI Web UI framework, and I think people might still
find it useful.
This script stopped working in 3.0 and I didn't notice that until just
now. This PR restores it to a functional state.
## Related Tickets & Documents
None
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Adds a new common component `IAIInformationPopover` that composes JSX to
be rendered within a popover as a tooltip. We were not able to use the
`Tooltip` component provided by chakra because you cannot interact with
elements within those (at least not that I could get working).
This just a sample over positive prompt. We need content from
@hipsterusername and @Millu before we can roll this out.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
This change enhances the invocation cache logic to delete cache entries
when the resources to which they refer are deleted.
For example, a cached output may refer to "some_image.png". If that
image is deleted, and this particular cache entry is later retrieved by
a node, that node's successors will receive references to the now
non-existent "some_image.png". When they attempt to use that image, they
will fail.
To resolve this, we need to invalidate the cache when the resources to
which it refers are deleted. Two options:
- Invalidate the whole cache on every image/latents/etc delete
- Selectively invalidate cache entries when their resources are deleted
Node outputs can be any shape, with any number of resource references in
arbitrarily nested pydantic models. Traversing that structure to
identify resources is not trivial.
But invalidating the whole cache is a bit heavy-handed. It would be nice
to be more selective.
Simple solution:
- Invocation outputs' resource references are always string identifiers
- like the image's or latents' name
- Invocation outputs can be stringified, which includes said identifiers
- When the invocation is cached, we store the stringified output
alongside the "live" output classes
- When a resource is deleted, pass its identifier to the cache service,
which can then invalidate any cache entries that refer to it
The images and latents storage services have been outfitted with
`on_deleted()` callbacks, and the cache service registers itself to
handle those events. This logic was copied from `ItemStorageABC`.
`on_changed()` callback are also added to the images and latents
services, though these are not currently used. Just following the
existing pattern.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
Reproduce the issue on main:
- Create a graph in workflow editor with two connected resize nodes
- Add an image to the first
- Enable cache on both
- Run the graph
- Clear Intermediates (in settings)
- Disable cache on the *second* node
- Run the graph, it should fail
Switch to the PR branch and start over, doing the exact same steps. You
shouldn't get any errors.
Example graph to start with:
## Added/updated tests?
- [~] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
* feat(ui): tweak queue UI components
* fix(ui): manually dispatch queue status query on queue item status change
RTK Query occasionally aborts the query that occurs when the tag is invalidated, especially if multples of them fire in rapid succession.
This resulted in the queue status and progress bar sometimes not reseting when the queue finishes its last item.
Manually dispatch the query now to get around this. Eventually should probably move this to a socket so we don't need to keep responding to socket with HTTP requests. Just send ti directly via socket
* chore(ui): remove errant console.logs
* fix(ui): do not accumulate node outputs in outputs area
* fix(ui): fix merge issue
---------
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Add `batch_id` to outbound events. This necessitates adding it to both `InvocationContext` and `InvocationQueueItem`. This allows the canvas to receive images.
When the user enqueues a batch on the canvas, it is expected that all images from that batch are directed to the canvas.
The simplest, most flexible solution is to add the `batch_id` to the invocation context-y stuff. Then everything knows what batch it came from, and we can have the canvas pick up images associated with its list of canvas `batch_id`s.
This change enhances the invocation cache logic to delete cache entries when the resources to which they refer are deleted.
For example, a cached output may refer to "some_image.png". If that image is deleted, and this particular cache entry is later retrieved by a node, that node's successors will receive references to the now non-existent "some_image.png". When they attempt to use that image, they will fail.
To resolve this, we need to invalidate the cache when the resources to which it refers are deleted. Two options:
- Invalidate the whole cache on every image/latents/etc delete
- Selectively invalidate cache entries when their resources are deleted
Node outputs can be any shape, with any number of resource references in arbitrarily nested pydantic models. Traversing that structure to identify resources is not trivial.
But invalidating the whole cache is a bit heavy-handed. It would be nice to be more selective.
Simple solution:
- Invocation outputs' resource references are always string identifiers - like the image's or latents' name
- Invocation outputs can be stringified, which includes said identifiers
- When the invocation is cached, we store the stringified output alongside the "live" output classes
- When a resource is deleted, pass its identifier to the cache service, which can then invalidate any cache entries that refer to it
The images and latents storage services have been outfitted with `on_deleted()` callbacks, and the cache service registers itself to handle those events. This logic was copied from `ItemStorageABC`.
`on_changed()` callback are also added to the images and latents services, though these are not currently used. Just following the existing pattern.
* fix(config): fix typing issues in `config/`
`config/invokeai_config.py`:
- use `Optional` for things that are optional
- fix typing of `ram_cache_size()` and `vram_cache_size()`
- remove unused and incorrectly typed method `autoconvert_path`
- fix types and logic for `parse_args()`, in which `InvokeAIAppConfig.initconf` *must* be a `DictConfig`, but function would allow it to be set as a `ListConfig`, which presumably would cause issues elsewhere
`config/base.py`:
- use `cls` for first arg of class methods
- use `Optional` for things that are optional
- fix minor type issue related to setting of `env_prefix`
- remove unused `add_subparser()` method, which calls `add_parser()` on an `ArgumentParser` (method only available on the `_SubParsersAction` object, which is returned from ArgumentParser.add_subparsers()`)
* feat: queued generation and batches
Due to a very messy branch with broad addition of `isort` on `main` alongside it, some git surgery was needed to get an agreeable git history. This commit represents all of the work on queued generation. See PR for notes.
* chore: flake8, isort, black
* fix(nodes): fix incorrect service stop() method
* fix(nodes): improve names of a few variables
* fix(tests): fix up tests after changes to batches/queue
* feat(tests): add unit tests for session queue helper functions
* feat(ui): dynamic prompts is always enabled
* feat(queue): add queue_status_changed event
* feat(ui): wip queue graphs
* feat(nodes): move cleanup til after invoker startup
* feat(nodes): add cancel_by_batch_ids
* feat(ui): wip batch graphs & UI
* fix(nodes): remove `Batch.batch_id` from required
* fix(ui): cleanup and use fixedCacheKey for all mutations
* fix(ui): remove orphaned nodes from canvas graphs
* fix(nodes): fix cancel_by_batch_ids result count
* fix(ui): only show cancel batch tooltip when batches were canceled
* chore: isort
* fix(api): return `[""]` when dynamic prompts generates no prompts
Just a simple fallback so we always have a prompt.
* feat(ui): dynamicPrompts.combinatorial is always on
There seems to be little purpose in using the combinatorial generation for dynamic prompts. I've disabled it by hiding it from the UI and defaulting combinatorial to true. If we want to enable it again in the future it's straightforward to do so.
* feat: add queue_id & support logic
* feat(ui): fix upscale button
It prepends the upscale operation to queue
* feat(nodes): return queue item when enqueuing a single graph
This facilitates one-off graph async workflows in the client.
* feat(ui): move controlnet autoprocess to queue
* fix(ui): fix non-serializable DOMRect in redux state
* feat(ui): QueueTable performance tweaks
* feat(ui): update queue list
Queue items expand to show the full queue item. Just as JSON for now.
* wip threaded session_processor
* feat(nodes,ui): fully migrate queue to session_processor
* feat(nodes,ui): add processor events
* feat(ui): ui tweaks
* feat(nodes,ui): consolidate events, reduce network requests
* feat(ui): cleanup & abstract queue hooks
* feat(nodes): optimize batch permutation
Use a generator to do only as much work as is needed.
Previously, though we only ended up creating exactly as many queue items as was needed, there was still some intermediary work that calculated *all* permutations. When that number was very high, the system had a very hard time and used a lot of memory.
The logic has been refactored to use a generator. Additionally, the batch validators are optimized to return early and use less memory.
* feat(ui): add seed behaviour parameter
This dynamic prompts parameter allows the seed to be randomized per prompt or per iteration:
- Per iteration: Use the same seed for all prompts in a single dynamic prompt expansion
- Per prompt: Use a different seed for every single prompt
"Per iteration" is appropriate for exploring a the latents space with a stable starting noise, while "Per prompt" provides more variation.
* fix(ui): remove extraneous random seed nodes from linear graphs
* fix(ui): fix controlnet autoprocess not working when queue is running
* feat(queue): add timestamps to queue status updates
Also show execution time in queue list
* feat(queue): change all execution-related events to use the `queue_id` as the room, also include `queue_item_id` in InvocationQueueItem
This allows for much simpler handling of queue items.
* feat(api): deprecate sessions router
* chore(backend): tidy logging in `dependencies.py`
* fix(backend): respect `use_memory_db`
* feat(backend): add `config.log_sql` (enables sql trace logging)
* feat: add invocation cache
Supersedes #4574
The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned.
## Results
This feature provides anywhere some significant to massive performance improvement.
The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal.
## Overview
A new `invocation_cache` service is added to handle the caching. There's not much to it.
All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching.
The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic.
To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key.
## In-Memory Implementation
An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts.
Max node cache size is added as `node_cache_size` under the `Generation` config category.
It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher.
Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them.
## Node Definition
The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`.
Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`.
The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something.
## One Gotcha
Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again.
If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit.
## Linear UI
The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs.
This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default.
This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`.
## Workflow Editor
All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user.
The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes.
Users should consider saving their workflows after loading them in and having them updated.
## Future Enhancements - Callback
A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not.
This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field.
## Future Enhancements - Persisted Cache
Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future.
* fix(ui): fix queue list item width
* feat(nodes): do not send the whole node on every generator progress
* feat(ui): strip out old logic related to sessions
Things like `isProcessing` are no longer relevant with queue. Removed them all & updated everything be appropriate for queue. May be a few little quirks I've missed...
* feat(ui): fix up param collapse labels
* feat(ui): click queue count to go to queue tab
* tidy(queue): update comment, query format
* feat(ui): fix progress bar when canceling
* fix(ui): fix circular dependency
* feat(nodes): bail on node caching logic if `node_cache_size == 0`
* feat(nodes): handle KeyError on node cache pop
* feat(nodes): bypass cache codepath if caches is disabled
more better no do thing
* fix(ui): reset api cache on connect/disconnect
* feat(ui): prevent enqueue when no prompts generated
* feat(ui): add queue controls to workflow editor
* feat(ui): update floating buttons & other incidental UI tweaks
* fix(ui): fix missing/incorrect translation keys
* fix(tests): add config service to mock invocation services
invoking needs access to `node_cache_size` to occur
* optionally remove pause/resume buttons from queue UI
* option to disable prepending
* chore(ui): remove unused file
* feat(queue): remove `order_id` entirely, `item_id` is now an autoinc pk
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description (edit by @blessedcoolant , @RyanJDick )
This PR adds support for IP-Adapters (a technique for image-based
prompts) in Invoke AI. Currently only available in the Node UI.
IP-Adapter Paper: [IP-Adapter: Text Compatible Image Prompt Adapter for
Text-to-Image Diffusion Models](https://arxiv.org/abs/2308.06721)
IP-Adapter reference code: https://github.com/tencent-ailab/IP-Adapter
On order to test, install the following models via the InvokeAI UI:
Image Encoders:
[InvokeAI/ip_adapter_sd_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder)
[InvokeAI/ip_adapter_sdxl_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sdxl_image_encoder)
IP-Adapters:
[InvokeAI/ip_adapter_sd15](https://huggingface.co/InvokeAI/ip_adapter_sd15)
[InvokeAI/ip_adapter_plus_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_sd15)
[InvokeAI/ip_adapter_plus_face_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15)
[InvokeAI/ip_adapter_sdxl](https://huggingface.co/InvokeAI/ip_adapter_sdxl)
Old instructions (for reference only):
> In order to test, you need to download and place the following models
in your InvokeAI models directory.
>
> - SD 1.5 - https://huggingface.co/h94/IP-Adapter/tree/main/models -->
Download the models and the `image_encoder` folder to
`models/core/ip_adapters/sd-1`
> - SDXL - https://huggingface.co/h94/IP-Adapter/tree/main/sdxl_models
-Download the models and the `image_encoder` folder to
`models/core/ip_adapaters/sdxl`
>
> This is only temporary. This needs to be handled differently. I
outlined them here.
https://github.com/invoke-ai/InvokeAI/pull/4429#issuecomment-1705776570
## Examples using this PR
### Image variations, no text prompt
Leftmost image in each row is original image used for input to
IP-Adapter. The other rows are example outputs with different seeds,
other parameters identical.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
A few Missed Translations From the Translation Update
## Related Tickets & Documents
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below.
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pull
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ X ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ X ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X ] No
## Description
Mask Edge was set to default, and producing poor results. I've updated
the default back to Unmasked.
Note: The target branch is `feat/ip-adapter`, not `main`. After a
cursory review here, I'll merge for an in-depth review as part of
https://github.com/invoke-ai/InvokeAI/pull/4429.
## Description
This branch adds model management support for IP-Adapter models. There
are a few notable/unusual aspects to how it is implemented:
- We have defined a model format that works better with our model
manager than the 'official' IP-Adapter repo, and will be hosting the
IP-Adapter models ourselves (See `invokeai/backend/ip_adapter/README.md`
for a description of the expected model formats.)
- The CLIP Vision models and IP-Adapter models are handled independently
in the model manager. The IP-Adapter model info has a reference to the
CLIP model that it is intended to be run with.
- The `BaseModelType.Any` field was added for CLIP Vision models, as
they don't have a clear 1-to-1 association with a particular base model.
## QA Instructions, Screenshots, Recordings
Install the following models via the InvokeAI UI:
Image Encoders:
-
[InvokeAI/ip_adapter_sd_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder)
-
[InvokeAI/ip_adapter_sdxl_image_encoder](https://huggingface.co/InvokeAI/ip_adapter_sdxl_image_encoder)
IP-Adapters:
-
[InvokeAI/ip_adapter_sd15](https://huggingface.co/InvokeAI/ip_adapter_sd15)
-
[InvokeAI/ip_adapter_plus_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_sd15)
-
[InvokeAI/ip_adapter_plus_face_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15)
-
[InvokeAI/ip_adapter_sdxl](https://huggingface.co/InvokeAI/ip_adapter_sdxl)
The immutable and serializable checks for redux can cause substantial performance issues. The immutable check in particular is pretty heavy. It's only run in dev mode, but this and really slow down the already-slower performance of dev mode.
The most important one for us is serializable, which has far less of a performance impact.
The immutable check is largely redundant because we use immer-backed RTK for everything and immer gives us confidence there.
Disable the immutable check, leaving serializable in.
A few weeks back, we changed how the canvas scales in response to changes in window/panel size.
This introduced a bug where if we the user hadn't already clicked the canvas tab once to initialize the stage elements, the stage's dimensions were zero, then the calculation of the stage's scale ends up zero, then something is divided by that zero and Konva dies.
This is only a problem on Chromium browsers - somehow Firefox handles it gracefully.
Now, when calculating the stage scale, never return a 0 - if it's a zero, return 1 instead. This is enough to fix the crash, but the image ends up centered on the top-left corner of the stage (the origin of the canvas).
Because the canvas elements are not initialized at this point (we haven't switched tabs yet), the stage dimensions fall back to (0,0). This means the center of the stage is also (0,0) - so the image is centered on (0,0), the top-left corner of the stage.
To fix this, we need to ensure we:
- Change to the canvas tab before actually setting the image, so the stage elements are able to initialize
- Use `flushSync` to flush DOM updates for this tab change so we actually have DOM elements to work with
- Update the stage dimensions once on first load of it (so in the effect that sets up the resize observer, we update the stage dimensions)
The result now is the expected behaviour - images sent to canvas do not crash and end up in the center of the canvas.
JSX is not serializable, so it cannot be in redux. Non-serializable global state may be put into `nanostores`.
- Use `nanostores` for `customStarUI`
- Use `nanostores` for `headerComponent`
- Re-enable the serializable & immutable check redux middlewares
* Update collections.py
RangeOfSizeInvocation was not taking step into account when generating the end point of the range
* - updated the node description to refelect this mod
- added a gt=0 constraint to ensure only a positive size of the range
- moved the + 1 to be on the size. To ensure the range is the requested size in cases where the step is negative
- formatted with Black
* Removed +1 from the range calculation
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* New classes to support the PromptsFromFileInvocation Class
- PromptPosNegOutput
- PromptSplitNegInvocation
- PromptJoinInvocation
- PromptReplaceInvocation
* - Added PromptsToFileInvocation,
- PromptSplitNegInvocation
- now counts the bracket depth so ensures it cout the numbr of open and close brackets match.
- checks for escaped [ ] so ignores them if escaped e.g \[
- PromptReplaceInvocation - now has a user regex. and no regex in made caseinsesitive
* Update prompt.py
created class PromptsToFileInvocationOutput and use it in PromptsToFileInvocation instead of BaseInvocationOutput
* Update prompt.py
* Added schema_extra title and tags for PromptReplaceInvocation, PromptJoinInvocation, PromptSplitNegInvocation and PromptsToFileInvocation
* Added PTFileds Collect and Expand
* update to nodes v1
* added ui_type to file_path for PromptToFile
* update params for the primitive types used, remove the ui_type filepath, promptsToFile now only accepts collections until a fix is available
* updated the parameters for the StringOutput primitive
* moved the prompt tools nodes out of the prompt.py into prompt_tools.py
* more rework for v1
* added github link
* updated to use "@invocation"
* updated tags
* Adde new nodes PromptStrength and PromptStrengthsCombine
* chore: black
* feat(nodes): add version to prompt nodes
* renamed nodes from prompt related to string related. Also moved them into a strings.py file. Also moved and renamed the PromptsFromFileInvocation from prompt.py to strings.py. The PTfileds still remain in the Prompt_tool.py for now.
* added , version="1.0.0" to the invocations
* removed the PTField related nodes and the prompt-tools.py file all new nodes now live in the
* formatted prompt.py and strings.py with Black and fixed silly mistake in the new StringSplitInvocation
* - Revert Prompt.py back to original
- Update strings.py to be only StringJoin, StringJoinThre, StringReplace, StringSplitNeg, StringSplit
* applied isort to imports
* fix(nodes): typos in `strings.py`
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
This maps values to labels for multiple-choice fields.
This allows "enum" fields (i.e. `Literal["val1", "val2", ...]` fields) to use code-friendly string values for choices, but present this to the UI as human-friendly labels.
* Added crop option to ImagePasteInvocation
ImagePasteInvocation extended the image with transparency when pasting outside of the base image's bounds. This introduces a new option to crop the resulting image back to the original base image.
* Updated version for ImagePasteInvocation as 3.1.1 was released.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
**NOTE!!!** This PR is against `feat/ip-adapter`, not `main`. I created
a PR because I made some pretty significant changes that I thought might
spark discussion.
I don't think it makes sense to do a full in-depth review here. If
possible, let's try to agree on the high-level approach and then merge
this and do an in-depth review on the original PR.
High-level changes:
- Split `IPAdapterField` from the `ControlField` and make them separate
inputs on the `DenoiseLatentsInvocation`
- Create context manager that handles patching/un-patching the UNet with
IP-Adapter attention blocks (`IPAdapter.apply_ip_adapter_attention()`)
- Pass IP-Adapter conditioning via `cross_attention_kwargs` rather than
concatenating it to the text embedding. This helps avoid breaking other
features (like long prompts).
- Remove unused blocks of the IP-Adapter implementation and do some
general tidying.
Out of scope:
- I haven't looked at model management yet. I'd like to get this merged
into `feat/ip-adapter` and then look at model management separately.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
There was an issue with the responsiveness of the quick links buttons in
the documentation.
## Related Tickets & Documents
- Related Issue #4455
- Closes#4455
## QA Instructions, Screenshots, Recordings
• On the documentation website, go to the Home page, scroll down to the
quick-links section.
[Home - InvokeAI Stable Diffusion Toolkit
Docs.webm](https://github.com/invoke-ai/InvokeAI/assets/92071471/0a7095c1-9d78-47f2-8da7-9c1e796bea3d)
## Added/updated tests?
- [ ] Yes
- [x] No : _It is a minor change in the documentation website._
## [optional] Are there any post deployment tasks we need to perform? No
We need to parse the config before doing anything related to invocations to ensure that the invocations union picks up on denied nodes.
- Move that to the top of api_app and cli_app
- Wrap subsequent imports in `if True:`, as a hack to satisfy flake8 and not have to noqa every line or the whole file
- Add tests to ensure graph validation fails when using a denied node, and that the invocations union does not have denied nodes (this indirectly provides confidence that the generated OpenAPI schema will not include denied nodes)
This simply hides nodes from the workflow editor. The nodes will still work if an API request is made with them. For example, you could hide `iterate` nodes from the workflow editor, but if the Linear UI makes use of those nodes, they will still function.
- Update `AppConfig` with optional property `nodesDenylist: string[]`
- If provided, nodes are filtered out by `type` in the workflow editor
Allow denying and explicitly allowing nodes. When a not-allowed node is used, a pydantic `ValidationError` will be raised.
- When collecting all invocations, check against the allowlist and denylist first. When pydantic constructs any unions related to nodes, the denied nodes will be omitted
- Add `allow_nodes` and `deny_nodes` to `InvokeAIAppConfig`. These are `Union[list[str], None]`, and may be populated with the `type` of invocations.
- When `allow_nodes` is `None`, allow all nodes, else if it is `list[str]`, only allow nodes in the list
- When `deny_nodes` is `None`, deny no nodes, else if it is `list[str]`, deny nodes in the list
- `deny_nodes` overrides `allow_nodes`
## What type of PR is this? (check all applicable)
3.1.1 Release build & updates
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Adds a configuration option to fetch metadata and workflows from api
isntead of the image file. Needed for commercial.
Minor corrections to spell and grammar in the feature request template.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because:
This PR should be self explanatory.
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Minor corrections to spell and grammar in the feature request template.
No code or behavioural changes.
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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N/A
## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
There are no tests for the issue template.
## [optional] Are there any post deployment tasks we need to perform?
I added extra steps to update the Cudnnn DLL found in the Torch package
because it wasn't optimised or didn't use the lastest version. So
manually updating it can speed up iteration but the result might differ
from each card. Exemple i passed from 3 it/s to a steady 20 it/s.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [x] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
fix(nodes): add version to iterate and collect
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
Scale Before Processing Dimensions now respect the Aspect Ratio that is
locked in. This makes it way easier to control the setting when using it
with locked ratios on the canvas.
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
Running the config script on Macs triggered an error due to absence of
VRAM on these machines! VRAM setting is now skipped.
## Added/updated tests?
- [ ] Yes
- [X] No : Will add this test in the near future.
I added extra steps to update the Cudnnn DLL found in the Torch package because it wasn't optimised or didn't use the lastest version. So manually updating it can speed up iteration but the result might differ from each card. Exemple i passed from 3 it/s to a steady 20 it/s.
@blessedcoolant Per discussion, have updated codeowners so that we're
not force merging things.
This will, however, necessitate a much more disciplined approval.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [X] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Add textfontimage node to communityNodes.md
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
fix(ui): fix non-nodes validation logic being applied to nodes invoke
button
For example, if you had an invalid controlnet setup, it would prevent
you from invoking on nodes, when node validation was disabled.
## Related Tickets & Documents
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below.
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- Closes
https://discord.com/channels/1020123559063990373/1028661664519831552/1148431783289966603
## What type of PR is this? (check all applicable)
- [x] Feature
- [x] Optimization
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
# Coherence Mode
A new parameter called Coherence Mode has been added to Coherence Pass
settings. This parameter controls what kind of Coherence Pass is done
after Inpainting and Outpainting.
- Unmasked: This performs a complete unmasked image to image pass on the
entire generation.
- Mask: This performs a masked image to image pass using your input mask
as the coherence mask.
- Mask Edge [DEFAULT] - This performs as masked image to image pass on
the edges of your mask to try and clear out the seams.
# Why The Coherence Masked Modes?
One of the issues with unmasked coherence pass arises when the diffusion
process is trying to align detailed or organic objects. Because Image to
Image tends change the image a little bit even at lower strengths, this
ends up in the paste back process being slightly misaligned. By
providing the mask to the Coherence Pass, we can try to eliminate this
in those cases. While it will be impossible to address this for every
image out there, having these options will allow the user to automate a
lot of this. For everything else there's manual paint over with inpaint.
# Graph Improvements
The graphs have now been refined quite a bit. We no longer do manual
blurring of the masks anymore for outpainting. This is no longer needed
because we now dilate the mask depending on the blur size while pasting
back. As a result we got rid of quite a few nodes that were handling
this in the older graph.
The graphs are also a lot cleaner now because we now tackle Scaled
Dimensions & Coherence Mode completely independently.
Inpainting result seem very promising especially with the Mask Edge
mode.
---
# New Infill Methods [Experimental]
We are currently trying out various new infill methods to see which ones
might perform the best in outpainting. We may keep all of them or keep
none. This will be decided as we test more.
## LaMa Infill
- Renabled LaMA infill in the UI.
- We are trying to get this to work without a memory overhead.
In order to use LaMa, you need to manually download and place the LaMa
JIT model in `models/core/misc/lama/lama.pt`. You can download the JIT
model from Sanster
[here](https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt)
and rename it to `lama.pt` or you can use the script in the original
LaMA repo to convert the base model to a JIT model yourself.
## CV2 Infill
- Added a new infilling method using CV2's Inpaint.
## Patchmatch Rescaling
Patchmatch infill input image is now downscaled and infilled. Patchmatch
can be really slow at large resolutions and this is a pretty decent way
to get around that. Additionally, downscaling might also provide a
better patch match by avoiding larger areas to be infilled with
repeating patches. But that's just the theory. Still testing it out.
## [optional] Are there any post deployment tasks we need to perform?
- If we decide to keep LaMA infill, then we will need to host the model
and update the installer to download it as a core model.
Adds my (@dwringer's) released nodes to the community nodes page.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Adds my released nodes -
Depth Map from Wavefront OBJ
Enhance Image
Generative Grammar-Based Prompt Nodes
Ideal Size Stepper
Image Compositor
Final Size & Orientation / Random Switch (Integers)
Text Mask (Simple 2D)
* Consolidated saturation/luminosity adjust.
Now allows increasing and inverting.
Accepts any color PIL format and channel designation.
* Updated docs/nodes/defaultNodes.md
* shortened tags list to channel types only
* fix typo in mode list
* split features into offset and multiply nodes
* Updated documentation
* Change invert to discrete boolean.
Previous math was unclear and had issues with 0 values.
* chore: black
* chore(ui): typegen
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Revised links to my node py files, replacing them with links to independent repos. Additionally I consolidated some nodes together (Image and Mask Composition Pack, Size Stepper nodes).
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
This PR is based on #4423 and should not be merged until it is merged.
[feat(nodes): add version to node
schemas](c179d4ccb7)
The `@invocation` decorator is extended with an optional `version` arg.
On execution of the decorator, the version string is parsed using the
`semver` package (this was an indirect dependency and has been added to
`pyproject.toml`).
All built-in nodes are set with `version="1.0.0"`.
The version is added to the OpenAPI Schema for consumption by the
client.
[feat(ui): handle node
versions](03de3e4f78)
- Node versions are now added to node templates
- Node data (including in workflows) include the version of the node
- On loading a workflow, we check to see if the node and template
versions match exactly. If not, a warning is logged to console.
- The node info icon (top-right corner of node, which you may click to
open the notes editor) now shows the version and mentions any issues.
- Some workflow validation logic has been shifted around and is now
executed in a redux listener.
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Closes#4393
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
-->
Loading old workflows should prompt a warning, and the node status icon
should indicate some action is needed.
## [optional] Are there any post deployment tasks we need to perform?
I've updated the default workflows:
- Bump workflow versions from 1.0 to 1.0.1
- Add versions for all nodes in the workflows
- Test workflows
[Default
Workflows.zip](https://github.com/invoke-ai/InvokeAI/files/12511911/Default.Workflows.zip)
I'm not sure where these are being stored right now @Millu
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
### Polymorphic Fields
Initial support for polymorphic field types. Polymorphic types are a
single of or list of a specific type. For example, `Union[str,
list[str]]`.
Polymorphics do not yet have support for direct input in the UI (will
come in the future). They will be forcibly set as Connection-only
fields, in which case users will not be able to provide direct input to
the field.
If a polymorphic should present as a singleton type - which would allow
direct input - the node must provide an explicit type hint.
For example, `DenoiseLatents`' `CFG Scale` is polymorphic, but in the
node editor, we want to present this as a number input. In the node
definition, the field is given `ui_type=UIType.Float`, which tells the
UI to treat this as a `float` field.
The connection validation logic will prevent connecting a collection to
`CFG Scale` in this situation, because it is typed as `float`. The
workaround is to disable validation from the settings to make this
specific connection. A future improvement will resolve this.
### Collection Fields
This also introduces better support for collection field types. Like
polymorphics, collection types are parsed automatically by the client
and do not need any specific type hints.
Also like polymorphics, there is no support yet for direct input of
collection types in the UI.
### Other Changes
- Disabling validation in workflow editor now displays the visual hints
for valid connections, but lets you connect to anything.
- Added `ui_order: int` to `InputField` and `OutputField`. The UI will
use this, if present, to order fields in a node UI. See usage in
`DenoiseLatents` for an example.
- Updated the field colors - duplicate colors have just been lightened a
bit. It's not perfect but it was a quick fix.
- Field handles for collections are the same color as their single
counterparts, but have a dark dot in the center of them.
- Field handles for polymorphics are a rounded square with dot in the
middle.
- Removed all fields that just render `null` from `InputFieldRenderer`,
replaced with a single fallback
- Removed logic in `zValidatedWorkflow`, which checked for existence of
node templates for each node in a workflow. This logic introduced a
circular dependency, due to importing the global redux `store` in order
to get the node templates within a zod schema. It's actually fine to
just leave this out entirely; The case of a missing node template is
handled by the UI. Fixing it otherwise would introduce a substantial
headache.
- Fixed the `ControlNetInvocation.control_model` field default, which
was a string when it shouldn't have one.
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
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- Closes#4266
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
Add this polymorphic float node to the end of your
`invokeai/app/invocations/primitives.py`:
```py
@invocation("float_poly", title="Float Poly Test", tags=["primitives", "float"], category="primitives")
class FloatPolyInvocation(BaseInvocation):
"""A float polymorphic primitive value"""
value: Union[float, list[float]] = InputField(default_factory=list, description="The float value")
def invoke(self, context: InvocationContext) -> FloatOutput:
return FloatOutput(value=self.value[0] if isinstance(self.value, list) else self.value)
``
Head over to nodes and try to connecting up some collection and polymorphic inputs.
- Node versions are now added to node templates
- Node data (including in workflows) include the version of the node
- On loading a workflow, we check to see if the node and template versions match exactly. If not, a warning is logged to console.
- The node info icon (top-right corner of node, which you may click to open the notes editor) now shows the version and mentions any issues.
- Some workflow validation logic has been shifted around and is now executed in a redux listener.
The `@invocation` decorator is extended with an optional `version` arg. On execution of the decorator, the version string is parsed using the `semver` package (this was an indirect dependency and has been added to `pyproject.toml`).
All built-in nodes are set with `version="1.0.0"`.
The version is added to the OpenAPI Schema for consumption by the client.
Initial support for polymorphic field types. Polymorphic types are a single of or list of a specific type. For example, `Union[str, list[str]]`.
Polymorphics do not yet have support for direct input in the UI (will come in the future). They will be forcibly set as Connection-only fields, in which case users will not be able to provide direct input to the field.
If a polymorphic should present as a singleton type - which would allow direct input - the node must provide an explicit type hint.
For example, `DenoiseLatents`' `CFG Scale` is polymorphic, but in the node editor, we want to present this as a number input. In the node definition, the field is given `ui_type=UIType.Float`, which tells the UI to treat this as a `float` field.
The connection validation logic will prevent connecting a collection to `CFG Scale` in this situation, because it is typed as `float`. The workaround is to disable validation from the settings to make this specific connection. A future improvement will resolve this.
This also introduces better support for collection field types. Like polymorphics, collection types are parsed automatically by the client and do not need any specific type hints.
Also like polymorphics, there is no support yet for direct input of collection types in the UI.
- Disabling validation in workflow editor now displays the visual hints for valid connections, but lets you connect to anything.
- Added `ui_order: int` to `InputField` and `OutputField`. The UI will use this, if present, to order fields in a node UI. See usage in `DenoiseLatents` for an example.
- Updated the field colors - duplicate colors have just been lightened a bit. It's not perfect but it was a quick fix.
- Field handles for collections are the same color as their single counterparts, but have a dark dot in the center of them.
- Field handles for polymorphics are a rounded square with dot in the middle.
- Removed all fields that just render `null` from `InputFieldRenderer`, replaced with a single fallback
- Removed logic in `zValidatedWorkflow`, which checked for existence of node templates for each node in a workflow. This logic introduced a circular dependency, due to importing the global redux `store` in order to get the node templates within a zod schema. It's actually fine to just leave this out entirely; The case of a missing node template is handled by the UI. Fixing it otherwise would introduce a substantial headache.
- Fixed the `ControlNetInvocation.control_model` field default, which was a string when it shouldn't have one.
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] No
## Description
Automatically infer the name of the model from the path supplied IF the
model name slot is empty. If the model name is not empty, we presume
that the user has entered a model name or made changes to it and we do
not touch it in order to not override user changes.
## Related Tickets & Documents
- Addresses: #4443
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
fix(ui): clicking node collapse button does not bring node to front
## Related Tickets & Documents
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below.
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- Related Issue
https://discord.com/channels/1020123559063990373/1130288930319761428/1147333454632071249
- Closes#4438
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
There is a call in `baseinvocation.invocation_output()` to
`cls.__annotations__`. However, in Python 3.9 not all objects have this
attribute. I have worked around the limitation in the way described in
https://docs.python.org/3/howto/annotations.html , which supposedly will
produce same results in 3.9, 3.10 and 3.11.
## Related Tickets & Documents
See
https://discord.com/channels/1020123559063990373/1146897072394608660/1146939182300799017
for first bug report.
## What type of PR is this? (check all applicable)
- [x] Cleanup
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Used https://github.com/albertas/deadcode to get rough overview of what
is not used, checked everything manually though. App still runs.
## Related Tickets & Documents
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below.
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- Closes#4424
## QA Instructions, Screenshots, Recordings
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software specifications as well as any other pertinent information.
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Ensure it doesn't explode when you run it.
* add StableDiffusionXLInpaintPipeline to probe list
* add StableDiffusionXLInpaintPipeline to probe list
* Blackified (?)
---------
Authored-by: Lincoln Stein <lstein@gmail.com>
Mucked about with to get it merged by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Add a click handler for node wrapper component that exclusively selects that node, IF no other modifier keys are held.
Technically I believe this means we are doubling up on the selection logic, as reactflow handles this internally also. But this is by far the most reliable way to fix the UX.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
The logic that introduced a circular import was actually extraneous. I
have entirely removed it.
This fixes the frontend lint test.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
This is the 3.1.0 release candidate. Minor bugfixes will be applied here
during testing and then merged into main upon release.
Copied into InvokeAI since IP-Adapter repo is not a package. Is there a better way to do this for non-packaged Python code while still keeping InvokeAI install easy?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
- Workflows are saved to image files directly
- Image-outputting nodes have an `Embed Workflow` checkbox which, if
enabled, saves the workflow
- `BaseInvocation` now has an `workflow: Optional[str]` field, so all
nodes automatically have the field (but again only image-outputting
nodes display this in UI)
- If this field is enabled, when the graph is created, the workflow is
stringified and set in this field
- Nodes should add `workflow=self.workflow` when they save their output
image to have the workflow written to the image
- Uploads now have their metadata retained so that you can upload
somebody else's image and have access to that workflow
- Graphs are no longer saved to images, workflows replace them
### TODO
- Images created in the linear UI do not have a workflow saved yet. Need
to write a function to build a workflow around the linear UI graph when
using linear tabs. Unfortunately it will not have the nice positioning
and size data the node editor gives you when you save a workflow...
we'll have to figure out how to handle this.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
All invocation metadata (type, title, tags and category) are now defined in decorators.
The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.
Category is a new invocation metadata, but it is not used by the frontend just yet.
- `@invocation()` decorator for invocations
```py
@invocation(
"sdxl_compel_prompt",
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
...
```
- `@invocation_output()` decorator for invocation outputs
```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
...
```
- update invocation docs
- add category to decorator
- regen frontend types
## What type of PR is this? (check all applicable)
- [x] Feature
- [x] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
- Keep Boards Modal open by default.
- Combine Coherence and Mask settings under Compositing
- Auto Change Dimensions based on model type (option)
- Size resets are now model dependent
- Add Set Control Image Height & Width to Width and Height option.
- Fix numerous color & spacing issues (especially those pertaining to
sliders being too close to the bottom)
- Add Lock Ratio Option
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
## QA Instructions, Screenshots, Recordings
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
Copied into InvokeAI since IP-Adapter repo is not a package. Is there a better way to do this for non-packaged Python code while still keeping InvokeAI install easy?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
In current main, long prompts and support for [Compel's `.and()`
syntax](https://github.com/damian0815/compel/blob/main/doc/syntax.md#conjunction)
is missing. This PR adds it back.
### needs Compel>=2.0.2.dev1
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
Send stuff directly from canvas to ControlNet
## Usage
- Two new buttons available on canvas Controlnet to import image and
mask.
- Click them.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Adds Next and Prev Buttons to the current image node
As usual you don't have to use 😄
## Related Tickets & Documents
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ X ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ X ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X ] No
## Description
Adds Seamless back into the options for Denoising.
## Related Tickets & Documents
- Related Issue #3975
## QA Instructions, Screenshots, Recordings
- Should test X, Y, and XY seamless tiling for all model architectures.
## Added/updated tests?
- [ ] Yes
- [ X ] No : Will need some guidance on automating this.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Allow an image and action to be passed into the app for starting state
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Fix masked generation with inpaint models
## Related Tickets & Documents
- Closes#4295
## Added/updated tests?
- [ ] Yes
- [x] No
Added a node to prompt Oobabooga Text-Generation-Webui
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [x] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Adds loading workflows with exhaustive validation via `zod`.
There is a load button but no dedicated save/load UI yet. Also need to add versioning to the workflow format itself.
## What type of PR is this? (check all applicable)
- [X Refactor
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
### Refactoring
This PR refactors `invokeai.app.services.config` to be easier to
maintain by splitting off the argument, environment and init file
parsing code from the InvokeAIAppConfig object. This will hopefully make
it easier for people to find the place where the various settings are
defined.
### New Features
In collaboration with @StAlKeR7779 , I have renamed and reorganized the
settings controlling image generation and model management to be more
intuitive. The relevant portion of the init file now looks like this:
```
Model Cache:
ram: 14.5
vram: 0.5
lazy_offload: true
Device:
precision: auto
device: auto
Generation:
sequential_guidance: false
attention_type: auto
attention_slice_size: auto
force_tiled_decode: false
```
Key differences are:
1. Split `Performance/Memory` into `Device`, `Generation` and `Model
Cache`
2. Added the ability to force the `device`. The value of this option is
one of {`auto`, `cpu`, `cuda`, `cuda:1`, `mps`}
3. Added the ability to force the `attention_type`. Possible values are
{`auto`, `normal`, `xformers`, `sliced`, `torch-sdp`}
4. Added the ability to force the `attention_slice_size` when `sliced`
attention is in use. The value of this option is one of {`auto`, `max`}
or an integer between 1 and 8.
@StAlKeR7779 Please confirm that I wired the `attention_type` and
`attention_slice_size` configuration options to the diffusers backend
correctly.
In addition, I have exposed the generation-related configuration options
to the TUI:
### Backward Compatibility
This refactor should be backward compatible with earlier versions of
`invokeai.yaml`. If the user re-runs the `invokeai-configure` script,
`invokeai.yaml` will be upgraded to the current format. Several
configuration attributes had to be changed in order to preserve backward
compatibility. These attributes been changed in the code where
appropriate. For the record:
| Old Name | Preferred New Name | Comment |
| ------------| ---------------|------------|
| `max_cache_size` | `ram_cache_size` |
| `max_vram_cache` | `vram_cache_size` |
| `always_use_cpu` | `use_cpu` | Better to check conf.device == "cpu" |
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
[fix(stats): fix fail case when previous graph is
invalid](d1d2d5a47d)
When retrieving a graph, it is parsed through pydantic. It is possible
that this graph is invalid, and an error is thrown.
Handle this by deleting the failed graph from the stats if this occurs.
[fix(stats): fix InvocationStatsService
types](1b70bd1380)
- move docstrings to ABC
- `start_time: int` -> `start_time: float`
- remove class attribute assignments in `StatsContext`
- add `update_mem_stats()` to ABC
- add class attributes to ABC, because they are referenced in instances
of the class. if they should not be on the ABC, then maybe there needs
to be some restructuring
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
On `main` (not this PR), create a situation in which an graph is valid
but will be rendered invalid on invoke. Easy way in node editor:
- create an `Integer Primitive` node, set value to 3
- create a `Resize Image` node and add an image to it
- route the output of `Integer Primitive` to the `width` of `Resize
Image`
- Invoke - this will cause first a `Validation Error` (expected), and if
you inspect the error in the JS console, you'll see it is a "session
retrieval error"
- Invoke again - this will also cause a `Validation Error`, but if you
inspect the error you should see it originates in the stats module (this
is the error this PR fixes)
- Fix the graph by setting the `Integer Primitive` to 512
- Invoke again - you get the same `Validation Error` originating from
stats, even tho there are no issues
Switch to this PR, and then you should only ever get the `Validation
Error` that that is classified as a "session retrieval error".
It is `"invocation"` for invocations and `"output"` for outputs. Clients may use this to confidently and positively identify if an OpenAPI schema object is an invocation or output, instead of using a potentially fragile heuristic.
Doing this via `BaseInvocation`'s `Config.schema_extra()` means all clients get an accurate OpenAPI schema.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
Doing this via these classes' `Config.schema_extra()` method makes it unintrusive and clients will get the correct types for these properties.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
The `type` property is required on all of them, but because this is defined in pydantic as a Literal, it is not required in the OpenAPI schema. Easier to fix this by changing the generated types than fiddling around with pydantic.
- move docstrings to ABC
- `start_time: int` -> `start_time: float`
- remove class attribute assignments in `StatsContext`
- add `update_mem_stats()` to ABC
- add class attributes to ABC, because they are referenced in instances of the class. if they should not be on the ABC, then maybe there needs to be some restructuring
When retrieving a graph, it is parsed through pydantic. It is possible that this graph is invalid, and an error is thrown.
Handle this by deleting the failed graph from the stats if this occurs.
Previously if an image was used in nodes and you deleted it, it would reset all of node editor. Same for controlnet.
Now it only resets the specific nodes or controlnets that used that image.
Add "nodrag", "nowheel" and "nopan" class names in interactable elements, as neeeded. This fixes the mouse interactions and also makes the node draggable from anywhere without needing shift.
Also fixes ctrl/cmd multi-select to support deselecting.
- move docstrings to ABC
- `start_time: int` -> `start_time: float`
- remove class attribute assignments in `StatsContext`
- add `update_mem_stats()` to ABC
- add class attributes to ABC, because they are referenced in instances of the class. if they should not be on the ABC, then maybe there needs to be some restructuring
When retrieving a graph, it is parsed through pydantic. It is possible that this graph is invalid, and an error is thrown.
Handle this by deleting the failed graph from the stats if this occurs.
## What type of PR is this? (check all applicable)
- [X] Feature
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
Follow symbolic links when auto importing from a directory. Previously
links to files worked, but links to directories weren’t entered during
the scanning/import process.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Should be removed when added in diffusers
https://github.com/huggingface/diffusers/pull/4599
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
PR to add Seam Painting back to the Canvas.
## TODO Later
While the graph works as intended, it has become extremely large and
complex. I don't know if there's a simpler way to do this. Maybe there
is but there's soo many connections and visualizing the graph in my head
is extremely difficult. We might need to create some kind of tooling for
this. Coz it's going going to get crazier.
But well works for now.
## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
This PR enhances the logging of performance statistics to include RAM
and model cache information. After each generation, the following will
be logged. The new information follows TOTAL GRAPH EXECUTION TIME.
```
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> Graph stats: 2408dbec-50d0-44a3-bbc4-427037e3f7d4
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> main_model_loader 1 0.004s 0.000G
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> clip_skip 1 0.002s 0.000G
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> compel 2 2.706s 0.246G
[2023-08-15 21:55:39,010]::[InvokeAI]::INFO --> rand_int 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> range_of_size 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> iterate 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> noise 1 0.003s 0.244G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> denoise_latents 1 2.429s 2.022G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> l2i 1 1.020s 1.858G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 6.171s
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> RAM used by InvokeAI process: 4.50G (delta=0.10G)
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> RAM used to load models: 1.99G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> VRAM in use: 0.303G
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> RAM cache statistics:
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Model cache hits: 2
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Model cache misses: 5
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Models cached: 5
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Models cleared from cache: 0
[2023-08-15 21:55:39,011]::[InvokeAI]::INFO --> Cache high water mark: 1.99/7.50G
```
There may be a memory leak in InvokeAI. I'm seeing the process memory
usage increasing by about 100 MB with each generation as shown in the
example above.
Previously the editor was using prop-drilling node data and templates to get values deep into nodes. This ended up causing very noticeable performance degradation. For example, any text entry fields were super laggy.
Refactor the whole thing to use memoized selectors via hooks. The hooks are mostly very narrow, returning only the data needed.
Data objects are never passed down, only node id and field name - sometimes the field kind ('input' or 'output').
The end result is a *much* smoother node editor with very minimal rerenders.
There is a tricky mouse event interaction between chakra's `useOutsideClick()` hook (used by chakra `<Menu />`) and reactflow. The hook doesn't work when you click the main reactflow area.
To get around this, I've used a dirty hack, copy-pasting the simple context menu component we use, and extending it slightly to respond to a global `contextMenusClosed` redux action.
- also implement pessimistic updates for starring, only changing the images that were successfully updated by backend
- some autoformat changes crept in
If `reactflow` initializes before the node templates are parsed, edges may not be rendered and the viewport may get reset.
- Add `isReady` state to `NodesState`. This is false when we are loading or parsing node templates and true when that is finished.
- Conditionally render `reactflow` based on `isReady`.
- Add `viewport` to `NodesState` & handlers to keep it synced. This allows `reactflow` to mount and unmount freely and not lose viewport.
Refine concept of "parameter" nodes to "primitives":
- integer
- float
- string
- boolean
- image
- latents
- conditioning
- color
Each primitive has:
- A field definition, if it is not already python primitive value. The field is how this primitive value is passed between nodes. Collections are lists of the field in node definitions. ex: `ImageField` & `list[ImageField]`
- A single output class. ex: `ImageOutput`
- A collection output class. ex: `ImageCollectionOutput`
- A node, which functions to load or pass on the primitive value. ex: `ImageInvocation` (in this case, `ImageInvocation` replaces `LoadImage`)
Plus a number of related changes:
- Reorganize these into `primitives.py`
- Update all nodes and logic to use primitives
- Consolidate "prompt" outputs into "string" & "mask" into "image" (there's no reason for these to be different, the function identically)
- Update default graphs & tests
- Regen frontend types & minor frontend tidy related to changes
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
On Windows systems, model merging was crashing at the very last step
with an error related to not being able to serialize a WindowsPath
object. I have converted the path that is passed to `save_pretrained`
into a string, which I believe will solve the problem.
Note that I had to rebuild the web frontend and add it to the PR in
order to test on my Windows VM which does not have the full node stack
installed due to space limitations.
## Related Tickets & Documents
https://discord.com/channels/1020123559063990373/1042475531079262378/1140680788954861698
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: it's smol
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
docker_entrypoint.sh does not quote variable expansion to prevent word
splitting, causing paths with spaces to fail as in #3913
## Related Tickets & Documents
#3913
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- Related Issue #3913
- Closes#3913
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [x] Refactor
- [x] Feature
- [x] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
- Remove SDXL raw prompt nodes
- SDXL and SD1/2 generation merged to same nodes - t2l/l2l
- Fixed - if no xformers installed we trying to enable attention
slicing, ignoring torch-sdp availability
- Fixed - In SDXL negative prompt now creating zeroed tensor(according
to official code)
- Added mask field to l2l node
- Removed inpaint node and all legacy code related to this node
- Pass info about seed in latents, so we can use it to initialize
ancestral/sde schedulers
- t2l and l2l nodes moved from strength to denoising_start/end
- Removed code for noise threshold(@hipsterusername said that there no
plans to restore this feature)
- Fixed - first preview image now not gray
- Fixed - report correct total step count in progress, added scheduler
order in progress event
- Added MaskEdge and ColorCorrect nodes (@hipsterusername)
## Added/updated tests?
- [ ] Yes
- [x] No
This is probably better done on the backend or in a different way. This can cause steps to go above 1000 which is more than the set number for the model.
This fixes an import issue introduced in commit 1bfe983. The change made
'invokeai_configure' into a module but this line still tries to call it
as if it's a function. This will result in a `'module' not callable`
error.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
imic from discord ask that I submit a PR to fix this bug.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
This fixes an import issue introduced in commit 1bfe983.
The change made 'invokeai_configure' into a module but this line still tries to call it as if it's a function. This will result in a `'module' not callable` error.
Seam options are now removed. They are replaced by two options --Mask Blur and Mask Blur Method .. which control the softness of the mask that is being painted.
During install testing I discovered two small problems in the
command-line scripts. These are fixed.
## What type of PR is this? (check all applicable)
- [X Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
-
## Have you updated all relevant documentation?
- [X] Yes
## Description
- installer - use correct entry point for invokeai-configure
- model merge script - prevent error when `--root` not provided
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Add support for LyCORIS IA3 format
## Related Tickets & Documents
- Closes#4229
## Added/updated tests?
- [ ] Yes
- [x] No
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] No - minor fix
## Have you updated all relevant documentation?
- [X] Yes
## Description
It turns out that some LoRAs do not have the text encoder model, and
this was causing the code that distinguishes the model base type during
model import to reject them as having an unknown base model. This PR
enables detection of these cases.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [s] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
was sorting with disabled at top of list instead of bottom
fixes#4217
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes#4217
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
There was no check at all to see if the canvas had a valid model already
selected. The first model in the list was selected every time.
Now, we check if its valid. If not, we go through the logic to try and
pick the first valid model.
If there are no valid models, or there was a problem listing models, the
model selection is cleared.
## Related Tickets & Documents
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below.
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- Closes#4125
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
-->
- Go to Canvas tab
- Select a model other than the first one in the list
- Go to a different tab
- Go back to Canvas tab
- The model should be the same as you selected
There was no check at all to see if the canvas had a valid model already selected. The first model in the list was selected every time.
Now, we check if its valid. If not, we go through the logic to try and pick the first valid model.
If there are no valid models, or there was a problem listing models, the model selection is cleared.
## What type of PR is this? (check all applicable)
- [X ] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
## Have you updated all relevant documentation?
- [X] Yes
## Description
This PR adds the `invokeai-import-images` script, which imports a
directory of 2.*.* -generated images into the current InvokeAI root
directory, preserving and converting their metadata. The script also
handles 3.* images.
Many thanks to @techjedi for writing this. This version differs from the
original in two minor respects:
1. It is installed as an `invokeai-import-images` command.
2. The prompts for image and database paths use file completion provided
by the `prompt_toolkit` library.
## To Test
1. Activate the virtual environment for the destination root to import
INTO
2. Run `invokeai-import-images`
3. Follow the prompts
## Related Tickets & Documents
This is a frequently-requested feature on Discord, but I couldn't find
an Issue.
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [X] No : but should in the future
## What type of PR is this? (check all applicable)
- [X ] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No - will be in release notes
## Description
On CUDA systems, this PR adds a new slider to the install-time configure
script for adjusting the VRAM cache and suggests a good starting value
based on the user's max VRAM (this is subject to verification).
On non-CUDA systems this slider is suppressed.
Please test on both CUDA and non-CUDA systems using:
```
invokeai-configure --root ~/invokeai-main/ --skip-sd --skip-support
```
To see and test the default values, move `invokeai.yaml` out of the way
before running.
**Note added 8 August 2023**
This PR also fixes the configure and model install scripts so that if
the window is too small to fit the user interface, the user will be
prompted to interactively resize the window and/or change font size
(with the option to give up). This will prevent `npyscreen` from
generating its horrible tracebacks.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X Yes
- [ ] No
## Description
If `models.yaml` is cleared out for some reason, the model manager will
repopulate it by scanning `models`. However, this would fail with a
pydantic validation error if any SDXL checkpoint models were present
because the lack of logic to pick the correct configuration file. This
has now been added.
## What type of PR is this? (check all applicable)
- [X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X No, because small fix
## Have you updated all relevant documentation?
- [X] Yes
## Description
A logic bug was introduced in PR #4109 that caused Web-based model
updates to fail with a pydantic validation error. This corrects the
problem.
## Related Tickets & Documents
PR #4109
* Fix hue adjustment
Hue adjustment wasn't working correctly because color channels got swapped. This has now been fixed and we're using PIL rather than cv2 to do the RGBA->HSV->RGBA conversion. The range of hue adjustment is also the more typical 0..360 degrees.
orphaned since #3550 removed the LazilyLoadedModelGroup code, probably unused since ModelCache took over responsibility for sequential offload somewhere around #3335.
ApiDependencies.invoker` provides typing for the API's services layer. Marking it `Optional` results in all the routes seeing it as optional, which is not good.
Instead of marking it optional to satisfy the initial assignment to `None`, we can just skip the initial assignment. This preserves the IDE hinting in API layer and is types-legal.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
At install time, when the user's config specified "auto" precision, the
installer was downloading the fp32 models even when an fp16 model would
be appropriate for the OS.
## Related Tickets & Documents
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below.
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- Closes#4127
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
## Description
Add lora loading for sdxl.
NOT TESTED - I run only 2 loras, please check more(including lycoris if
they already exists).
## QA Instructions, Screenshots, Recordings
https://civitai.com/models/118536/voxel-xl
## Added/updated tests?
- [ ] Yes
- [x] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [X] Feature
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR adds execution time and VRAM usage reporting to each graph
invocation. The log output will look like this:
```
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader 1 0.005s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip 1 0.004s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel 2 0.512s 0.26G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l 1 3.541s 1.93G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i 1 0.679s 0.58G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 4.749s
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
```
On systems without CUDA, the VRAM stats are not printed.
The current implementation keeps track of graph ids separately so will
not be confused when several graphs are executing in parallel. It
handles exceptions, and it is integrated into the app framework by
defining an abstract base class and storing an implementation instance
in `InvocationServices`.
multi-select actions include:
- drag to board to move all to that board
- right click to add all to board or delete all
backend changes:
- add routes for changing board for list of image names, deleting list of images
- change image-specific routes to `images/i/{image_name}` to not clobber other routes (like `images/upload`, `images/delete`)
- subclass pydantic `BaseModel` as `BaseModelExcludeNull`, which excludes null values when calling `dict()` on the model. this fixes inconsistent types related to JSON parsing null values into `null` instead of `undefined`
- remove `board_id` from `remove_image_from_board`
frontend changes:
- multi-selection stuff uses `ImageDTO[]` as payloads, for dnd and other mutations. this gives us access to image `board_id`s when hitting routes, and enables efficient cache updates.
- consolidate change board and delete image modals to handle single and multiples
- board totals are now re-fetched on mutation and not kept in sync manually - was way too tedious to do this
- fixed warning about nested `<p>` elements
- closes#4088 , need to handle case when `autoAddBoardId` is `"none"`
- add option to show gallery image delete button on every gallery image
frontend refactors/organisation:
- make typegen script js instead of ts
- enable `noUncheckedIndexedAccess` to help avoid bugs when indexing into arrays, many small changes needed to satisfy TS after this
- move all image-related endpoints into `endpoints/images.ts`, its a big file now, but this fixes a number of circular dependency issues that were otherwise felt impossible to resolve
Currently we use some workflow trigger conditionals to run either a real test workflow (installing the app and running it) or a fake workflow, disguised as the real one, that just auto-passes.
This change refactors the workflow to use a single workflow that can be skipped, using another github action to determine which things to run depending on the paths changed.
## What type of PR is this? (check all applicable)
- [x] Refactor
## Have you discussed this change with the InvokeAI team?
- [x] No, because it's pretty minor
## Have you updated all relevant documentation?
- [x] No
## Description
This PR just moves the PR template to within the `.github/` directory
leading to a overall minimal project structure.
## Added/updated tests?
- [x] No : because this change doesn't affect or need a separate test
- Create abstract base class InvocationStatsServiceBase
- Store InvocationStatsService in the InvocationServices object
- Collect and report stats on simultaneous graph execution
independently for each graph id
- Track VRAM usage for each node
- Handle cancellations and other exceptions gracefully
## What type of PR is this? (check all applicable)
- [ X] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ X] No, because: invisible change
## Have you updated all relevant documentation?
- [ X] Yes
- [ ] No
## Description
There was a problem in 3.0.1 with root resolution. If INVOKEAI_ROOT were
set to "." (or any relative path), then the location of root would
change if the code did an os.chdir() after config initialization. I
fixed this in a quick and dirty way for 3.0.1.post3.
This PR cleans up the code with a little refactoring.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ]X Bug Fix
- [ ] Optimization
- [ X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ X] No, because: obvious problem
## Have you updated all relevant documentation?
- [ X] Yes
- [ ] No
## Description
The manual installation documentation in both README.md and
020_MANUAL_INSTALL give an incomplete `invokeai-configure` command which
leaves out the path to the root directory to create. As a result, the
invokeai root directory gets created in the user’s home directory, even
if they intended it to be placed somewhere else.
This is a fairly important issue.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [x] Feature
- [x] Bug Fix
- [?] Optimization
## Have you discussed this change with the InvokeAI team?
- [x] No
## Description
- Fixed filter type select using `images` instead of `all` -- Probably
some merge conflict.
- Added loading state for model lists. Handy when the model list takes
longer than a second for any reason to fetch. Better to show this than
an empty screen.
- Refactored the render model list function so we modify the display
component in one area rather than have repeated code.
### Other Issues
- The list can get a bit laggy on initial load when you have a hundreds
of models / loras. This needs to be fixed. Will make another PR for
this.
## What type of PR is this? (check all applicable)
- [x] Refactor
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: trivial
## Description
Adds a few obviously missing `Optional` on fields that default to
`None`.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: Just a documentation update
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Updated documentation with a getting started guide & a glossary of terms
needed to get started
Updated the landing page flow for users
<img width="1430" alt="Screenshot 2023-07-27 at 9 53 25 PM"
src="https://github.com/invoke-ai/InvokeAI/assets/7254508/d0006ba7-2ed4-4044-a1bc-ca9a99df9397">
## Related Tickets & Documents
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close an issue, please include them
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
This is a relatively stable release that corrects the urgent windows
install and model manager problems in 3.0.1. It still has two known
bugs:
1. Many inpainting models are not loading correctly.
2. The merge script is failing to start.
- Remove FaceMask and add link FaceTools repository, which includes FaceMask, FaceOff, and FacePlace
- Move Ideal Size up from under the template entry
## What type of PR is this? (check all applicable)
- [ X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [X] Yes - bug discovered by jpphoto
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X] Not needed
## Description
The user can customize the location of the models directory by setting
configuration variable `models_dir`. However, the model manager and the
TUI installer were all treating model relative paths as relative to the
invokeai root rather than the designated models directory. This has been
fixed by changing path resolution calls from using `config.root_path` to
`config.models_path`
Unfortunately there were many instances that needed replacement, so this
needs a bit of functional testing - try adding models, removing models,
renaming them, converting checkpoints, etc.
## What type of PR is this? (check all applicable)
- [ X] Optimization
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X ] Yes
- [ ] No
## Description
This PR does two things:
1. if the environment variable INVOKEAI_ROOT is defined at install time,
the zipfile installer will default to its value when asking the user
where to install the software
2. If the user has more than 72 models of any type installed, then the
list will be truncated in the TUI and the user given a warning. Anything
larger than this number of models causes the vertical space to overflow.
The only effect of truncation is that the user will not be able to see
and delete the models that were truncated. The message advises the user
to go to the Web Model Manager interface in this event.
## What type of PR is this? (check all applicable)
- [X ] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X] No
## Description
This PR fixes several issues with the 3.0.0 conversion script:
- Handles checkpoint variants that don't put dots between fields in the
long state dict key names
- Handles ema, non-ema, pruned and non-pruned ckpts
- Does not add safety checker to converted checkpoints
- Respects precision of original checkpoint file
## What type of PR is this? (check all applicable)
- [ X] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] Not needed
## Description
Windows users have been getting a lot of OSErrors while installing 3.0.1
during the pip dependency installation phase. Generally the errors have
involved just two packages, pydantic and numpy. Looking at the install
logs, I see that both of these packages are first installed under one
version number by a dependency, and then uninstalled and replaced by a
slightly different version specified in invoke's `pyproject.toml`. I
think this is the problem - maybe the earlier package is not completely
closed before it is uninstalled and reinstalled.
This PR relaxes pinning of numpy and pydantic in `pyproject.toml`.
Everything seems to install and run properly. Hopefully it will address
the windows install bug as well.
## What type of PR is this? (check all applicable)
- [x] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
- SDXL Metadata was not being retrieved. This PR fixes it.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
not yet, making pr to show
## Have you updated relevant documentation?
- [ ] Yes
- [ ] No
## Description
Temp Change Node String input to a textbox, to allow easier input of
prompts and larger strings, it works for me but please tell me if I did
it wrong and if the size is ok
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: minor fix, let me know your thoughts
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
## Related Tickets & Documents
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below.
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- Related Issue # https://github.com/invoke-ai/InvokeAI/issues/4017
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [x] No : Requires mps device
## [optional] Are there any post deployment tasks we need to perform?
Please test on an MPS (M1/M2) device.
Relevant code causing the error in #401701b6ec21fa/src/diffusers/schedulers/scheduling_euler_discrete.py (L263C3-L268C75)
```
self.sigmas = torch.from_numpy(sigmas).to(device=device)
if str(device).startswith("mps"):
# mps does not support float64
self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)
else:
self.timesteps = torch.from_numpy(timesteps).to(device=device)
```
## What type of PR is this? (check all applicable)
- [x] Bug Fix
## Description
- Fix SDXL Concat Link animation not considering the fact that prompt
boxes can be resized.
- Also fixed a minor issue where the overlaying animation box would
block the prompt input resize slightly. Should be good now.
## What type of PR is this? (check all applicable)
- [X ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
## Have you updated all relevant documentation?
- [X ] Yes
## Description
Added solutions for installation issues related to large SDXL files and
Windows dependency glitches.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Making the prompt area styling match across all tabs / models and move
all prompt related components into a parent components for quick add.
Cherry picked stuff from the Styles PR coz we ain't gonna merge that.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
- make the `SDXLConcatLink` icon match existing colors in light mode
- make the link toggle button accent color when active (its not super obvious but at least there is *some* visual difference for the button)
## What type of PR is this? (check all applicable)
- [ X] Feature
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
## Have you updated all relevant documentation?
- [ X] Yes - this makes invokeai behave the way it is described in
LOGGING.md
## Description
Prior to this PR, the uvicorn embedded web server did all its logging
independently of the InvokeAI logging infrastructure, and none of the
command-line or `invokeai.yaml` configuration directives, such as
`log_level` had any effect on its output. This PR replaces the uvicorn
logger with InvokeAI's, simultaneously creating a more uniform output
experience, as well as a unified way to control the amount and
destination of the logs.
Here's what we used to see at startup:
```
[2023-07-27 07:29:48,027]::[InvokeAI]::INFO --> InvokeAI version 3.0.1rc2
[2023-07-27 07:29:48,027]::[InvokeAI]::INFO --> Root directory = /home/lstein/invokeai-main
[2023-07-27 07:29:48,028]::[InvokeAI]::INFO --> GPU device = cuda NVIDIA GeForce RTX 4070
[2023-07-27 07:29:48,040]::[InvokeAI]::INFO --> Scanning /home/lstein/invokeai-main/models for new models
[2023-07-27 07:29:49,263]::[InvokeAI]::INFO --> Scanned 22 files and directories, imported 10 models
[2023-07-27 07:29:49,271]::[InvokeAI]::INFO --> Model manager service initialized
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:9090 (Press CTRL+C to quit)
INFO: 127.0.0.1:44404 - "GET /socket.io/?EIO=4&transport=polling&t=OcN7Pvd HTTP/1.1" 200 OK
INFO: 127.0.0.1:44404 - "POST /socket.io/?EIO=4&transport=polling&t=OcN7Pw6&sid=SB-NsBKLSrW7YtM0AAAA HTTP/1.1" 200 OK
INFO: ('127.0.0.1', 44418) - "WebSocket /socket.io/?EIO=4&transport=websocket&sid=SB-NsBKLSrW7YtM0AAAA" [accepted]
INFO: connection open
INFO: 127.0.0.1:44430 - "GET /socket.io/?EIO=4&transport=polling&t=OcN7Pw9&sid=SB-NsBKLSrW7YtM0AAAA HTTP/1.1" 200 OK
INFO: 127.0.0.1:44404 - "GET /socket.io/?EIO=4&transport=polling&t=OcN7PwU&sid=SB-NsBKLSrW7YtM0AAAA HTTP/1.1" 200 OK
INFO: 127.0.0.1:44404 - "GET /api/v1/images/?is_intermediate=true HTTP/1.1" 200 OK
INFO: 127.0.0.1:43448 - "GET / HTTP/1.1" 200 OK
INFO: connection closed
INFO: 127.0.0.1:43448 - "GET /assets/index-5a784cdd.js HTTP/1.1" 200 OK
INFO: 127.0.0.1:43458 - "GET /assets/favicon-0d253ced.ico HTTP/1.1" 304 Not Modified
INFO: 127.0.0.1:43448 - "GET /locales/en.json HTTP/1.1" 200 OK
```
And here's what we see with the new implementation:
```
[2023-07-27 12:05:28,810]::[uvicorn.error]::INFO --> Started server process [875161]
[2023-07-27 12:05:28,810]::[uvicorn.error]::INFO --> Waiting for application startup.
[2023-07-27 12:05:28,810]::[InvokeAI]::INFO --> InvokeAI version 3.0.1rc2
[2023-07-27 12:05:28,810]::[InvokeAI]::INFO --> Root directory = /home/lstein/invokeai-main
[2023-07-27 12:05:28,811]::[InvokeAI]::INFO --> GPU device = cuda NVIDIA GeForce RTX 4070
[2023-07-27 12:05:28,823]::[InvokeAI]::INFO --> Scanning /home/lstein/invokeai-main/models for new models
[2023-07-27 12:05:29,970]::[InvokeAI]::INFO --> Scanned 22 files and directories, imported 10 models
[2023-07-27 12:05:29,977]::[InvokeAI]::INFO --> Model manager service initialized
[2023-07-27 12:05:29,980]::[uvicorn.error]::INFO --> Application startup complete.
[2023-07-27 12:05:29,981]::[uvicorn.error]::INFO --> Uvicorn running on http://127.0.0.1:9090 (Press CTRL+C to quit)
[2023-07-27 12:05:32,140]::[uvicorn.access]::INFO --> 127.0.0.1:45236 - "GET /socket.io/?EIO=4&transport=polling&t=OcO6ILb HTTP/1.1" 200
[2023-07-27 12:05:32,142]::[uvicorn.access]::INFO --> 127.0.0.1:45248 - "GET /socket.io/?EIO=4&transport=polling&t=OcO6ILb HTTP/1.1" 200
[2023-07-27 12:05:32,154]::[uvicorn.access]::INFO --> 127.0.0.1:45236 - "POST /socket.io/?EIO=4&transport=polling&t=OcO6ILr&sid=13O4-5uLx5eP-NuqAAAA HTTP/1.1" 200
[2023-07-27 12:05:32,168]::[uvicorn.access]::INFO --> 127.0.0.1:45252 - "POST /socket.io/?EIO=4&transport=polling&t=OcO6IM0&sid=0KRqxmh7JLc8t7wZAAAB HTTP/1.1" 200
[2023-07-27 12:05:32,171]::[uvicorn.error]::INFO --> ('127.0.0.1', 45264) - "WebSocket /socket.io/?EIO=4&transport=websocket&sid=0KRqxmh7JLc8t7wZAAAB" [accepted]
[2023-07-27 12:05:32,172]::[uvicorn.error]::INFO --> connection open
[2023-07-27 12:05:32,174]::[uvicorn.access]::INFO --> 127.0.0.1:45276 - "GET /socket.io/?EIO=4&transport=polling&t=OcO6IM3&sid=0KRqxmh7JLc8t7wZAAAB HTTP/1.1" 200
```
You can also divert everything to a file with a `invokeai.yaml` entry
like this:
```
Logging:
log_handlers:
- file=/home/lstein/invokeai/logs/access_log
log_format: plain
log_level: info
```
This works with syslog and other log handlers as well.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
https://github.com/huggingface/diffusers/releases/tag/v0.19.0
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
pull
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X ] Yes
- [ ] No
## Description
This updates InvokeAI's pyproject.toml to the minimum library versions
needed to support Python 3.11. It updates the installer to find and
allow for 3.11, and the documentation.
Between 3.10 and 3.11 there was a change to the handling of `enum`
interpolation into strings that caused the model manager to break. I
think I have fixed the places where this was a problem, but there may be
other instances in which this will cause problems. Please be alert for
errors involving `ModelType` or `BaseModelType`.
I also took the opportunity to add a `SilenceWarnings()` context to the
t2i and i2i invocations. This quenches nags from diffusers about the
HuggingFace NSFW library.
I have tested basic functionality (t2i, i2i, inpaint, lora, controlnet,
nodes) on 3.10 and 3.11 and all seems good. Please test more
extensively!
## Added/updated tests?
- [ X ] Yes - existing tests run to completion
- [ ] No
## [optional] Are there any post deployment tasks we need to perform?
Should be a drop-in replacement.
* add upper bound for minWidth to prevent crash with cypress
* add fallback so UI doesnt crash when backend isnt running
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
when multiple python versions are installed with `pyenv`, the executable
(shim) exists, but returns an error when trying to run it
unless activated with `pyenv`. This commit ensures the python
executable is usable.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature (dev feature and reformatting)
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Introducing black to the code base as a first step towards this:
https://github.com/invoke-ai/InvokeAI/discussions/3721
## Related Tickets & Documents
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below.
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pull
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [x] No : Not applicable
## [optional] Are there any post deployment tasks we need to perform?
All active branches will be affected by this and will need to be
updated.
This PR adds a new github workflow for black as well as config for
pre-commit hooks to those who wish to use it
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X ] Not needed
## Description
This bugfix enables InvokeAI to convert sd-1, sd-2 and sdxl base model
checkpoints (.safetensors) to diffusers.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X ] No
## Description
This PR causes the installer to install, by default, the fine-tuned
SDXL-1.0 VAE located at
https://huggingface.co/madebyollin/sdxl-vae-fp16-fix.
Although this VAE is supposed to run at fp16 resolution, currently it
only works in InvokeAI at fp32. However, because it is a fine tune, it
may have fewer of the watermark-related artifacts that we see with the
SDXL-1.0 VAE.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X] Not necessary
## Description
When adding new core models to a 3.0.0 root directory needed to support
SDXL, the configure script was (under some conditions) overwriting
models.yaml. This PR corrects the problem.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X ] Yes
- [ ] No
## Description
I have reworked the console TUIs for the configure and model install
scripts to require much less vertical space. In the event that the
"NEXT" button is still missing and "page 1/2" is displayed, scrolling
beyond the last checkbox will now automatically move to page 2 where the
buttons are displayed. This is not ideal, but will no longer block user
completely.
If users continue to have problems after this, I'll get rid of the TUI
altogether and replace with a web form.
## Added/updated tests?
- [ ] Yes
- [X ] No : not needed
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X ] No, because they trust me
## Have you updated all relevant documentation?
- [ X] Yes
- [ ] No
## Description
* Added the RAIL++ license for SDXL
* Updated configure script with URLs for both the original RAIL-M and
RAIL++ licenses
* Added invisible watermark documentation and renamed doc file
* Updated documentation for installation
* Updated documentation on settings in invokeai.yaml
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
Metadata was not getting saved coz the accumulator was not plugged in if
watermark or nsfw nodes were turned off.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ x] No, because there was no time!
## Have you updated all relevant documentation?
- [ ] Yes
- [X ] No
## Description
Hotfix for issue of SD1 and SD2 legacy safetensors models not converting
in 3.0.1rc1.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X ] Yes
- [] No
## Description
This PR adds NSFW checker and invisible watermark fields. The NSFW
checker takes an image input and produces an image output. If NSFW
content is detected, the output image will be blurred and a "caution"
icon pasted into its upper left corner. A boolean `active` field
controls whether the checker is active. If turned off it simply returns
a copy of the image.
The invisible watermark node adds an invisible text to the image,
defaulting to "InvokeAI". To decode the watermark use the
`invisible-watermark` command, which is part of the
`invisible-watermark` library:
```
$ invisible-watermark -v -a decode -t bytes -m dwtDct -l 64 ./bluebird-watermark.png
decode time ms: 14.129877090454102
InvokeAI
```
Note that the `-l` (length) argument is mandatory. It is set to 64 here
because the watermark `InvokeAI` is 8 bytes/64 bits long. The length
must match in order for the watermark to be decoded correctly.
Both nodes are now incorporated into the linear Text2Image and
Image2Image UIs, including the canvas. They are not implemented for
inpaint currently.
The nodes can be disabled with configuration options:
```
invisible_watermark: false
nsfw_checker: false
```
or at launch time with `--no-invisible_watermark` and
`--no-nsfw_checker`.
feat(ui) use `as` for menuitem links
I had requested this be done with the chakra `Link` component, but actually using `as` is correct according to the docs. For other components, you are supposed to use `Link` but looks like `MenuItem` has this built in.
Fixed in all places where we use it.
Also:
- fix github icon
- give menu hamburger button padding
- add menu motion props so it animates the same as other menus
feat(ui): restore ColorModeButton
@maryhipp
chore(ui): lint
feat(ui): remove colormodebutton again
sry
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ X] No - not yet WIP
## Description
This PR adds support for loading and converting checkpoint-format
ControlNet and SDXL models. The SDXL and SDXL-refiner model conversions
are working; however saving the unet in safetensors format leads to
corrupted model files, so currently is saving in .bin format (after
scanning the input model).
ControlNet conversion seems to be working but needs further testing.
To use this PR, you will need to copy the files
`invokeai/configs/stable-diffusion/sd_xl_base.yaml` and
`invokeai/configs/stable-diffusion/sd_xl_refiner.yaml` into
`INVOKEAI/configs/stable-diffusion`. You will also need to run
`invokeai-configure --yes --skip-sd` in order to install additional core
model files needed by the converter.
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
- Update the Aspect Ratio tags to show the aspect ratio values rather
than Wide / Square and etc.
- Updated Lora Input to take values between -50 and 50 coz I found some
LoRA that are actually trained to work until -25 and +15 too. So these
input caps should mostly suffice. If there's ever a LoRA that goes
bonkers on that, we can change it.
- Fixed LoRA's being sorted the wrong way in Lora Select.
- Fixed Embeddings being sorted the wrong way in Embedding Select.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
- add `addNSFWCheckerToGraph` and `addWatermarkerToGraph` functions
- use them in all linear graph creation
- add state & toggles to settings modal to enable these
- trigger queries for app config on socket connect
- disable the nsfw/watermark booleans if we get the app config and they are not available
## What type of PR is this? (check all applicable)
- [x] Feature
## Have you discussed this change with the InvokeAI team?
- [x] Yes
## Description
This PR adds support for SDXL Models in the Linear UI
### DONE
- SDXL Base Text To Image Support
- SDXL Base Image To Image Support
- SDXL Refiner Support
- SDXL Relevant UI
## [optional] Are there any post deployment tasks we need to perform?
Double check to ensure nothing major changed with 1.0 -- In any case
those changes would be backend related mostly. If Refiner is scrapped
for 1.0 models, then we simply disable the Refiner Graph.
Rolled back the earlier split of the refiner model query.
Now, when you use `useGetMainModelsQuery()`, you must provide it an array of base model types.
They are provided as constants for simplicity:
- ALL_BASE_MODELS
- NON_REFINER_BASE_MODELS
- REFINER_BASE_MODELS
Opted to just use args for the hook instead of wrapping the hook in another hook, we can tidy this up later if desired.
We can derive `isRefinerAvailable` from the query result (eg are there any refiner models installed). This is a piece of server state, so by using the list models response directly, we can avoid needing to manually keep the client in sync with the server.
Created a `useIsRefinerAvailable()` hook to return this boolean wherever it is needed.
Also updated the main models & refiner models endpoints to only return the appropriate models. Now we don't need to filter the data on these endpoints.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No
## Description
Updated script to close stale issues with the newest version of the
actions/stale
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [X] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
Not sure how this script gets kicked off
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: This is a minor fix that I happened upon while
reading
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Within the `mkdocs.yml` file, there's a typo where `Model Merging` is
spelled as `Model Mergeing`. I also found some unnecessary white space
that I removed.
## Related Tickets & Documents
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- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : Not big enough of a change to require tests (unless it is)
## [optional] Are there any post deployment tasks we need to perform?
Might need to re-run the yml file for docs to regenerate, but I'm hardly
familiar with the codebase so 🤷
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: n/a
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No n/a
## Description
Add a generation mode indicator to canvas.
- use the existing logic to determine if generation is txt2img, img2img,
inpaint or outpaint
- technically `outpaint` and `inpaint` are the same, just display
"Inpaint" if its either
- debounce this by 1s to prevent jank
I was going to disable controlnet conditionally when the mode is inpaint
but that involves a lot of fiddly changes to the controlnet UI
components. Instead, I'm hoping we can get inpaint moved over to latents
by next release, at which point controlnet will work.
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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https://github.com/invoke-ai/InvokeAI/assets/4822129/87464ae9-4136-4367-b992-e243ff0d05b4
## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
n/a
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No, n/a
## Description
When a queue item is popped for processing, we need to retrieve its
session from the DB. Pydantic serializes the graph at this stage.
It's possible for a graph to have been made invalid during the graph
preparation stage (e.g. an ancestor node executes, and its output is not
valid for its successor node's input field).
When this occurs, the session in the DB will fail validation, but we
don't have a chance to find out until it is retrieved and parsed by
pydantic.
This logic was previously not wrapped in any exception handling.
Just after retrieving a session, we retrieve the specific invocation to
execute from the session. It's possible that this could also have some
sort of error, though it should be impossible for it to be a pydantic
validation error (that would have been caught during session
validation). There was also no exception handling here.
When either of these processes fail, the processor gets soft-locked
because the processor's cleanup logic is never run. (I didn't dig deeper
into exactly what cleanup is not happening, because the fix is to just
handle the exceptions.)
This PR adds exception handling to both the session retrieval and node
retrieval and events for each: `session_retrieval_error` and
`invocation_retrieval_error`.
These events are caught and displayed in the UI as toasts, along with
the type of the python exception (e.g. `Validation Error`). The events
are also logged to the browser console.
## Related Tickets & Documents
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below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
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Closes#3860 , #3412
## QA Instructions, Screenshots, Recordings
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software specifications as well as any other pertinent information.
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Create an valid graph that will become invalid during execution. Here's
an example:
This is valid before execution, but the `width` field of the `Noise`
node will end up with an invalid value (`0`). Previously, this would
soft-lock the app and you'd have to restart it.
Now, with this graph, you will get an error toast, and the app will not
get locked up.
## Added/updated tests?
- [x] Yes (ish)
- [ ] No
@Kyle0654 @brandonrising
It seems because the processor runs in its own thread, `pytest` cannot
catch exceptions raised in the processor.
I added a test that does work, insofar as it does recreate the issue.
But, because the exception occurs in a separate thread, the test doesn't
see it. The result is that the test passes even without the fix.
So when running the test, we see the exception:
```py
Exception in thread invoker_processor:
Traceback (most recent call last):
File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
File "/usr/lib/python3.10/threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "/home/bat/Documents/Code/InvokeAI/invokeai/app/services/processor.py", line 50, in __process
self.__invoker.services.graph_execution_manager.get(
File "/home/bat/Documents/Code/InvokeAI/invokeai/app/services/sqlite.py", line 79, in get
return self._parse_item(result[0])
File "/home/bat/Documents/Code/InvokeAI/invokeai/app/services/sqlite.py", line 52, in _parse_item
return parse_raw_as(item_type, item)
File "pydantic/tools.py", line 82, in pydantic.tools.parse_raw_as
File "pydantic/tools.py", line 38, in pydantic.tools.parse_obj_as
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
```
But `pytest` doesn't actually see it as an exception. Not sure how to
fix this, it's a bit beyond me.
## [optional] Are there any post deployment tasks we need to perform?
nope don't think so
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
`search_for_models` is explicitly typed as taking a singular `Path` but
was given a list because some later function in the stack expects a
list. Fixed that to be compatible with the paths. This is the only use
of that function.
The `list()` call is unrelated but removes a type warning since it's
supposed to return a list, not a set. I can revert it if requested.
This was found through pylance type errors. Go types!
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
This import is missing and used later in the file.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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below.
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No: n/a
## Description
At some point I typo'd this and set the max seed to signed int32 max. It
should be *un*signed int32 max.
This restored the seed range to what it was in v2.3.
Also fixed a bug in the Noise node which resulted in the max valid seed
being one less than intended.
## Related Tickets & Documents
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below.
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- Related Issues
#2843 is against v2.3 and increases the range of valid seeds
substantially. Maybe we can explore this in the future but as of v3.0,
we use numpy for a RNG in a few places, and it maxes out at the max
`uint32`. I will close this PR as this supersedes it.
- Closes#3866
## QA Instructions, Screenshots, Recordings
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You should be able to use seeds up to and including `4294967295`.
## Added/updated tests?
- [ ] Yes
- [x] No : don't think we have any relevant tests
## [optional] Are there any post deployment tasks we need to perform?
nope!
At some point I typo'd this and set the max seed to signed int32 max. It should be *un*signed int32 max.
This restored the seed range to what it was in v2.3.
## What type of PR is this? (check all applicable)
- [x] Bug Fix
## Have you discussed this change with the InvokeAI team?
- [x] Yes, we feel very passionate about this.
## Description
Uploading an incorrect JSON file to the Node Editor would crash the app.
While this is a much larger problem that we will tackle while refining
the Node Editor, this is a fix that should address 99% of the cases out
there.
When saving an InvokeAI node graph, there are three primary keys.
1. `nodes` - which has all the node related data.
2. `edges` - which has all the edges related data
3. `viewport` - which has all the viewport related data.
So when we load back the JSON, we now check if all three of these keys
exist in the retrieved JSON object. While the `viewport` itself is not a
mandatory key to repopulate the graph, checking for it will allow us to
treat it as an additional check to ensure that the graph was saved from
InvokeAI.
As a result ...
- If you upload an invalid JSON file, the app now warns you that the
JSON is invalid.
- If you upload a JSON of a graph editor that is not InvokeAI, it simply
warns you that you are uploading a non InvokeAI graph.
So effectively, you should not be able to load any graph that is not
generated by ReactFlow.
Here are the edge cases:
- What happens if a user maintains the above key structure but tampers
with the data inside them? Well tested it. Turns out because we validate
and build the graph based on the JSON data, if you tamper with any data
that is needed to rebuild that node, it simply will skip that and load
the rest of the graph with valid data.
- What happens if a user uploads a graph that was made by some other
random ReactFlow app? Well, same as above. Because we do not have to
parse that in our setup, it simply will skip it and only display what
are setup to do.
I think that just about covers 99% of the cases where this could go
wrong. If there's any other edges cases, can add checks if need be. But
can't think of any at the moment.
## Related Tickets & Documents
### Closes
- #3893
- #3881
## [optional] Are there any post deployment tasks we need to perform?
Yes. Making @psychedelicious a little bit happier. :P
- use the existing logic to determine if generation is txt2img, img2img, inpaint or outpaint
- technically `outpaint` and `inpaint` are the same, just display
"Inpaint" if its either
- debounce this by 1s to prevent jank
When a queue item is popped for processing, we need to retrieve its session from the DB. Pydantic serializes the graph at this stage.
It's possible for a graph to have been made invalid during the graph preparation stage (e.g. an ancestor node executes, and its output is not valid for its successor node's input field).
When this occurs, the session in the DB will fail validation, but we don't have a chance to find out until it is retrieved and parsed by pydantic.
This logic was previously not wrapped in any exception handling.
Just after retrieving a session, we retrieve the specific invocation to execute from the session. It's possible that this could also have some sort of error, though it should be impossible for it to be a pydantic validation error (that would have been caught during session validation). There was also no exception handling here.
When either of these processes fail, the processor gets soft-locked because the processor's cleanup logic is never run. (I didn't dig deeper into exactly what cleanup is not happening, because the fix is to just handle the exceptions.)
This PR adds exception handling to both the session retrieval and node retrieval and events for each: `session_retrieval_error` and `invocation_retrieval_error`.
These events are caught and displayed in the UI as toasts, along with the type of the python exception (e.g. `Validation Error`). The events are also logged to the browser console.
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: n/a
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No n/a
## Description
Big cleanup:
- improve & simplify the app logging
- resolve all TS issues
- resolve all circular dependencies
- fix all lint/format issues
## QA Instructions, Screenshots, Recordings
`yarn lint` passes:
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
bask in the glory of what *should* be a fully-passing frontend lint on
this PR
Added the Ideal Size node
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: It's a community node addition
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
Added a reference to my community node that calculates the ideal size
for initial latent generation that avoids duplication. This is the logic
that was present in 2.3.5's first pass of high-res optimization.
## Related Tickets & Documents
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below.
For example having the text: "closes #1234" would connect the current
pull
request to issue 1234. And when we merge the pull request, Github will
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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software specifications as well as any other pertinent information.
-->
## Added/updated tests?
- [ ] Yes
- [X] No : This is a documentation change that references my community
node.
## [optional] Are there any post deployment tasks we need to perform?
Add Face Mask to communityNodes.md
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [x] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No
## Description
Add Face Mask to communituNodes.md list.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: just updated docs to try to help lead new users to
installs a little easier
## Have you updated relevant documentation?
- [x] Yes
- [ ] No
## Description
Some minor docs tweaks
## Related Tickets & Documents
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [x] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Revised boards logic and UI
## Related Tickets & Documents
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below.
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- Related Issue # discord convos
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
On mps generating images with resolution above ~1536x1536 results in
"fried" output. Main problem that such resolution results in tensors in
size more then 4gb. Looks like that some of mps internals can't handle
properly this, so to mitigate it I break attention calculation in
chunks.
## QA Instructions, Screenshots, Recordings
Example of bad output:
## What type of PR is this? (check all applicable)
- [ X] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Description
This is a WIP to collect documentation enhancements and other polish
prior to final 3.0.0 release. Minor bug fixes may go in here if
non-controversial. It should be merged into main prior to the final
release.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [x] Bug Fix
## Desc
Fixes a bug where the board name is not displayed in the header if there
are no images in it.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Add progress preview for sdxl generation nodes
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X ] Yes
- [ ] No, because:
## Have you updated relevant documentation?
- [ X] Yes (swagger)
- [ ] No
## Description
This add new routes for getting and setting the command line console
logging level.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [X] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes Discussed with @hipsterusername yesterday
- [ ] No, because:
## Have you updated relevant documentation?
- [ ] Yes
- [X] No Not yet (but change to default ControlNet resizing doesn't
require any user documentation)
## Description
This PR adds resize modes (just_resize, crop_resize, fill_resize) to
InvokeAI's ControlNet node. The implementation is largely based on
lllyasviel's, which includes a high quality resizer specifically
intended to handle common ControlNet preprocessor outputs, such as
binary (black/white) images, grayscale images, and binary or grayscale
thin lines. Previously the InvokeAI ControlNet implementation only did a
simple resize with independent x/y scaling to match noise latent.
### "just_resize" mode (the default setting)
With the new implementation, using the default "just_resize" mode,
ControlNet images are still resized with independent x/y scaling to
match the noise latent resolution, but with the high quality resizer. As
a result, images generated in InvokeAI now look much closer to
counterparts generated via sd-webui-controlnet. See example below. All
inference runs are using prompt="old man", same ControlNet canny edge
detection preprocessor and model and control image, identical other
parameters except for control_mode. The top row is previous simple
resize implementation, the bottom row is with new high quality resizer
and "just_resize" mode. Control_mode is: left="balanced", middle="more
prompt", right="more control". The high quality resize images are
identical (at least by eye) to output from sd-webui-controlnet with same
settings.
## "crop_resize" and "fill_resize" modes
The other two resize modes are "crop_resize" and "fill_resize". Whereas
"just_resize" ignores any aspect ratio mismatch between the ControlNet
image and the noise latent, these other modes preserve the aspect ratio
of the ControlNet image. The "crop_resize" mode does this by cropping
the image, and the "fill_resize" option does this by expanding the image
(adding fill pixels). See example below. In this case all inference runs
are using prompt="old man", the ControlNet Midas depth detection
preprocessor and depth model, same control image of size 512x512,
control_mode="balanced", and identical other parameters except for
resize_mode and noise latent dimensions. For top row noise latent size
is 768x512, and for bottom row noise latent size is 512x768. Resize_mode
is: left="just_resize", middle="crop_resize", right="fill_resize"
## Are there any post deployment tasks we need to perform?
To use "just_resize" mode in linear UI, no post deployment work is
needed. The default is switched from old resizer to new high quality
resizer.
To use "just_resize", "crop_resize", and "fill_resize" modes in node UI,
no post deployment work is needed. There is also an additional option
"just_resize_simple" that uses old resizer, mainly left in for testing
and for anyone curious to see the difference.
To use "crop_resize" and "fill_resize" in linear UI, there will need to
be some work to incorporate choice of three modes in ControlNet UI
(probably best to not expose "just_resize_simple" in linear UI, it just
confuses things).
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Description
This changes the "sync" route from a GET to POST method, in keeping with
the Representational Existential(?) State Transfer (REST) protocol.
* feat(ui): enhance clear intermediates feature
- retrieve the # of intermediates using a new query (just uses list images endpoint w/ limit of 0)
- display the count in the UI
- add types for clearIntermediates mutation
- minor styling and verbiage changes
* feat(ui): remove unused settings option for guides
* feat(ui): use solid badge variant
consistent with the rest of the usage of badges
* feat(ui): update board ctx menu, add board auto-add
- add context menu to system boards - only open is select board. did this so that you dont think its broken when you click it
- add auto-add board. you can right click a user board to enable it for auto-add, or use the gallery settings popover to select it. the invoke button has a tooltip on a short delay to remind you that you have auto-add enabled
- made useBoardName hook, provide it a board id and it gets your the board name
- removed `boardIdToAdTo` state & logic, updated workflows to auto-switch and auto-add on image generation
* fix(ui): clear controlnet when clearing intermediates
* feat: Make Add Board icon a button
* feat(db, api): clear intermediates now clears all of them
* feat(ui): make reset webui text subtext style
* feat(ui): board name change submits on blur
---------
Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because: documentation update that needs review from the team
before going live
## Description
I updated the contribution guidelines, adding more structure and a
getting started guide. Also re-organized the tabs to be in the order of
most commonly used.
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
run `mkdocs serve` to check it out
## Added/updated tests?
- [ ] Yes
- [X ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Description
ImageToLatentsInvocation defaulted to float16 rather than detect the
requested precision from configs.
This caused an exception to be raised on systems that don't support
float16 (e.g. CPU).
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
* feat(ui): migrate listImages to RTK query using createEntityAdapter
- see comments in `endpoints/images.ts` for explanation of the caching
- so far, only manually updating `all` images when new image is generated. no other manual cache updates are implemented, but will be needed.
- fixed some weirdness with loading state components (like the spinners in gallery)
- added `useThumbnailFallback` for `IAIDndImage`, this displays the tiny webp thumbnail while the full-size images load
- comment out some old thunk related stuff in gallerySlice, which is no longer needed
* feat(ui): add manual cache updates for board changes (wip)
- update RTK Query caches when adding/removing single image to/from board
- work more on migrating all image-related operations to RTK Query
* update AddImagesToBoardContext so that it works when user uses context menu + modal
* handle case where no image is selected
* get assets working for main list and boards - dnd only
* feat(ui): migrate image uploads to RTK Query
- minor refactor of `ImageUploader` and `useImageUploadButton` hooks, simplify some logic
- style filesystem upload overlay to match existing UI
- replace all old `imageUploaded` thunks with `uploadImage` RTK Query calls, update associated logic including canvas related uploads
- simplify `PostUploadAction`s that only need to display user input
* feat(ui): remove `receivedPageOfImages` thunks
* feat(ui): remove `receivedImageUrls` thunk
* feat(ui): finish removing all images thunks
stuff now broken:
- image usage
- delete board images
- on first load, no image selected
* feat(ui): simplify `updateImage` cache manipulation
- we don't actually ever change categories, so we can remove a lot of logic
* feat(ui): simplify canvas autosave
- instead of using a network request to set the canvas generation as not intermediate, we can just do that in the graph
* feat(ui): simplify & handle edge cases in cache updates
* feat(db, api): support `board_id='none'` for `get_many` images queries
This allows us to get all images that are not on a board.
* chore(ui): regen types
* feat(ui): add `All Assets`, `No Board` boards
Restructure boards:
- `all images` is all images
- `all assets` is all assets
- `no board` is all images/assets without a board set
- user boards may have images and assets
Update caching logic
- much simpler without every board having sub-views of images and assets
- update drag and drop operations for all possible interactions
* chore(ui): regen types
* feat(ui): move download to top of context menu
* feat(ui): improve drop overlay styles
* fix(ui): fix image not selected on first load
- listen for first load of all images board, then select the first image
* feat(ui): refactor board deletion
api changes:
- add route to list all image names for a board. this is required to handle board + image deletion. we need to know every image in the board to determine the image usage across the app. this is fetched only when the delete board and images modal is opened so it's as efficient as it can be.
- update the delete board route to respond with a list of deleted `board_images` and `images`, as image names. this is needed to perform accurate clientside state & cache updates after deleting.
db changes:
- remove unused `board_images` service method to get paginated images dtos for a board. this is now done thru the list images endpoint & images service. needs a small logic change on `images.delete_images_on_board`
ui changes:
- simplify the delete board modal - no context, just minor prop drilling. this is feasible for boards only because the components that need to trigger and manipulate the modal are very close together in the tree
- add cache updates for `deleteBoard` & `deleteBoardAndImages` mutations
- the only thing we cannot do directly is on `deleteBoardAndImages`, update the `No Board` board. we'd need to insert image dtos that we may not have loaded. instead, i am just invalidating the tags for that `listImages` cache. so when you `deleteBoardAndImages`, the `No Board` will re-fetch the initial image limit. i think this is more efficient than e.g. fetching all image dtos to insert then inserting them.
- handle image usage for `deleteBoardAndImages`
- update all (i think/hope) the little bits and pieces in the UI to accomodate these changes
* fix(ui): fix board selection logic
* feat(ui): add delete board modal loading state
* fix(ui): use thumbnails for board cover images
* fix(ui): fix race condition with board selection
when selecting a board that doesn't have any images loaded, we need to wait until the images haveloaded before selecting the first image.
this logic is debounced to ~1000ms.
* feat(ui): name 'No Board' correctly, change icon
* fix(ui): do not cache listAllImageNames query
if we cache it, we can end up with stale image usage during deletion.
we could of course manually update the cache as we are doing elsewhere. but because this is a relatively infrequent network request, i'd like to trade increased cache mgmt complexity here for increased resource usage.
* feat(ui): reduce drag preview opacity, remove border
* fix(ui): fix incorrect queryArg used in `deleteImage` and `updateImage` cache updates
* fix(ui): fix doubled open in new tab
* fix(ui): fix new generations not getting added to 'No Board'
* fix(ui): fix board id not changing on new image when autosave enabled
* fix(ui): context menu when selection is 0
need to revise how context menu is triggered later, when we approach multi select
* fix(ui): fix deleting does not update counts for all images and all assets
* fix(ui): fix all assets board name in boards list collapse button
* fix(ui): ensure we never go under 0 for total board count
* fix(ui): fix text overflow on board names
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
* new route to clear intermediates
* UI to clear intermediates from settings modal
* cleanup
* PR feedback
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Description
In transformers 4.31.0 `text_model.embeddings.position_ids` no longer
part of state_dict.
Fix untested as can't run right now but should be correct. Also need to
check how transformers 4.30.2 works with this fix.
## Related Tickets & Documents
8e5d1619b3 (diff-7f53db5caa73a4cbeb0dca3b396e3d52f30f025b8c48d4daf51eb7abb6e2b949R191)https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_buffer
## QA Instructions, Screenshots, Recordings
```
File "C:\Users\artis\Documents\invokeai\.venv\lib\site-packages\invokeai\backend\model_management\convert_ckpt_to_diffusers.py", line 844, in convert_ldm_clip_checkpoint
text_model.load_state_dict(text_model_dict)
File "C:\Users\artis\Documents\invokeai\.venv\lib\site-packages\torch\nn\modules\module.py", line 2041, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for CLIPTextModel:
Unexpected key(s) in state_dict: "text_model.embeddings.position_ids".
```
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
## Description
Fix for
```
File "/home/invokeuser/InvokeAI/invokeai/app/services/processor.py",
line 70, in __process
outputs = invocation.invoke(
File "/home/invokeuser/InvokeAI/invokeai/app/invocations/latent.py",
line 660, in invoke
device=choose_torch_device()
NameError: name 'choose_torch_device' is not defined
```
when using scale latents node
## Related Tickets & Documents
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- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ X] Yes
- [ ] No, because:
## Description
This PR points mkdocs to the `main` branch again, so that the 3.0.0
documentation appears in gh-pages.
It also makes a minor tweak to the tooltip for model imports, so that
users know that URLs are accepted.
Also rebuilds frontend for use in beta testing.
I've opted to leave out any additional upscaling parameters like scale
and denoising strength, which, from my review of the ESRGAN code, don't
do much:
- scale just resizes the image using CV2 after the AI upscaling, so
that's not particularly useful
- denoising strength is only valid for one class of model, which we are
no longer supporting
If there is demand, we can implement output size/scale UI and handle it
by passing the upscaled image to that a resize/scale node.
I also understand we previously had some functionality to blend the
upscaled image with the original. If that is desired, we would need to
implement that as a node that we can pass the upscaled image to.
Demo:
https://github.com/invoke-ai/InvokeAI/assets/4822129/32eee615-62a1-40ce-a183-87e7d935fbf1
---
[feat(nodes): add RealESRGAN_x2plus.pth, update upscale
nodes](dbc256c5b4)
- add `RealESRGAN_x2plus.pth` model to installer @lstein
- add `RealESRGAN_x2plus.pth` to `realesrgan` node
- rename `RealESRGAN` to `ESRGAN` in nodes
- make `scale_factor` optional in `img_scale` node
[feat(ui): restore ad-hoc
upscaling](b3fd29e5ad)
- remove face restoration entirely
- add dropdown for ESRGAN model select
- add ad-hoc upscaling graph and workflow
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Description
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
There no vram cleanup on models offload which leads to filling vram and
slow generation speed.
## What type of PR is this? (check all applicable)
- [x] Feature
- [x] Optimization
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Description
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [x] Optimization
- [ ] Documentation Update
## Description
Various fixes to consume less memory and make run sdxl on 8gb vram.
Most changes due to moving all output tensors to cpu, so that cached
tensors not consume vram.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Fixes a bug in the `inpaint` node introduced by the new version of
`compel`. The other nodes were updated, but this one was missed. Fixed
by @StAlKeR7779 ty
## Related Tickets & Documents
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- Related Issue # discord reports
- Closes #
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : n/a, bugfix
This contains minor fixes to the beta as well as the version bump to
3.0.0.
Fixes include:
- Warning user when the installer window size is inadequate for the TUI.
- Selection of the most frequently downloaded controlnet models for
default installation.
- Adding the LowRA LoRA for dark image enhancement
- Documentation
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
Making some final style fixes before we push the next 3.0 version
tomorrow.
- Fixed light mode colors in Settings Modal.
- Double checked other light mode colors. Nothing seems off.
- Added Base Model badge to the model list item. Makes it visually
better and also serves as a quick glance feature for the user.
- Some minor styling updates to the Node Editor.
- Fixed hotkeys 'G' and 'O', 'Shift+G' and 'Shift+O' used to toggle the
panels not resizing canvas. #3780
- Fixed hotkey 'N' not working for Snap To Grid on Canvas.
- Fixed brush opacity hotkeys not working.
- Cleaned up hotkeys modal of hotkeys that are no longer used.
- Updated compel requirement to `2.0.0`
## Related Tickets & Documents
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- Related Issue #
- Closes#3780
## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
hides sdxl models from linear ui model select. just a hold-me-over
## Related Tickets & Documents
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## Added/updated tests?
- [ ] Yes
- [x] No : n/a
## [optional] Are there any post deployment tasks we need to perform?
- add `RealESRGAN_x2plus.pth` model to installer
- add `RealESRGAN_x2plus.pth` to `realesrgan` node
- rename `RealESRGAN` to `ESRGAN` in nodes
- make `scale_factor` optional in `img_scale` node
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [x] No, because:
If its not useful, they do not have to use it 😄
## Description
While I was still in the viewportcontrols.tsx
added Option to toggle off the minimap with default being on(true)
added Tooltips to the buttons in viewportcontrols.tsx
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
This is a WIP to add SDXL support.
Tasks:
- [x] SDXL model loading support
- [x] SDXL model installation
- [x] SDXL model loader
- [x] SDXL base invocations for text2latent and latent2latent
- [ ] SDXL refiner invocations for text2latent and latent2latent
- [x] Compel support / pooled embeddings
- [ ] Linear UI graph for SDXL
- [ ] Documentation
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Description
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
fix json formatting to not have big red comment blocks
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because: simple docs fix
## Description
Fix LOCAL_DEVELOPMENT.md json comment highlighting
## Related Tickets & Documents
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## QA Instructions, Screenshots, Recordings
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## Added/updated tests?
- [ ] Yes
- [x] No : simple docs change
This PR completely ports over the Model Manager to 3.0 -- all of the
functionality has now been restored in addition to the following
changes.
- Model Manager now has been moved to its own tab on the left hand side.
- Model Manager has three tabs - Model Manager, Import Models and Merge
Models
- The edit forms for the Models now allow the users to update the model
name and the base model too along with other details.
- Checkpoint Edit form now displays the available config files from
InvokeAI and also allows users to supply their own custom config file.
- Under Import Models you can directly add models or a scan a folder for
your checkpoint files.
- Adding models has two modes -- Simple and Advanced.
- In Simple Mode, you just simply need to pass a path and InvokeAI will
try to determine kind of model it is and fill up the rest of the details
accordingly. This input lets you supply local paths to diffusers / local
paths to checkpoints / huggingface repo ID's to download models /
CivitAI links.
- Simple Mode also allows you to download different models types like
VAE's and Controlnet models and etc. Not just main models.
- In cases where the auto detection system of InvokeAI fails to read a
model correctly, you can take the manual approach and go to Advanced
where you can configure your model while adding it exactly the way you
want it. Both Diffusers and Checkpoint models now have their own custom
forms.
- Scan Models has been cleaned up. It will now only display the models
that are not already installed to InvokeAI. And each item will have two
options - Quick Add and Advanced .. replicating the Add Model behavior
from above.
- Scan Models now has a search bar for you to search through your
scanned models.
- Merge Models functionality has been restored.
This is a wrap for this PR.
**TODO: (Probably for 3.1)**
- Add model management for model types such as VAE's and ControlNet
Models
- Replace the VAE slot on the edit forms with the installed VAE drop
down + custom option
[feat(nodes): emit model loading
events](7b6159f8d6)
- remove dependency on having access to a `node` during emits, would
need a bit of additional args passed through the system and I don't
think its necessary at this point. this also allowed us to drop an
extraneous fetching/parsing of the session from db.
- provide the invocation context to all `get_model()` calls, so the
events are able to be emitted
- test all model loading events in the app and confirm socket events are
received
[feat(ui): add listeners for model load
events](c487166d9c)
- currently only exposed as DEBUG-level logs
---
One change I missed in the commit messages is the `ModelInfo` class is
not serializable, so I split out the pieces of information we didn't
already have (hash, location, precision) and added them to the event
payload directly.
This small patch improves the stability of `invokeai-*` scripts by
avoiding crashes in the model manager while scanning the models
directory for new and removed models.
Both support the same actions:
- Open in new tab
- Copy image (if supported by browser)
- Use prompt
- Use seed
- Use all
- Send to img2img
- Send to canvas
- Change board
- Download image
- Delete
- restore copy image functionality* in image context menu, current image buttons
- give IAIDndImage the same context menu
* copying image to clipboard is not possible on Firefox unless the user enables a setting which is disabled by default. if the browser does not support copying an image, the copy functionality is disabled.
- filename -> file_path
- pre and post prompt changed to optional
- clearer pre and post prompt descriptions
- handle pre and post prompt passed as None
- max_prompts defaults to 1 isted of 0 to avoid accidentally processing large prompt files with it set to 0 when adding a new node.
This PR adds several default models to the ones selected at install
time. It also removes the GFPGAN and text2clip models, which should
shave a little time off the install process.
## ESRGAN:
* models/core/upscaling/realesrgan/RealESRGAN_x4plus.pth
* models/core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth
*
models/core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth
## ControlNet
* models/sd-1/controlnet/canny
* models/sd-1/controlnet/depth
* models/sd-1/controlnet/lineart
* models/sd-1/controlnet/openpose
## Embedding (textual inversion)
* models/sd-1/embedding/EasyNegative.safetensors
- remove dependency on having access to a `node` during emits, would need a bit of additional args passed through the system and I don't think its necessary at this point. this also allowed us to drop an extraneous fetching/parsing of the session from db.
- provide the invocation context to all `get_model()` calls, so the events are able to be emitted
- test all model loading events in the app and confirm socket events are received
- update controlnet state to use object format for model
- update model-parsing helper functions to log errors
- update nodes components, types and state
- remove controlnets from state when models are loaded and the controlnet's model is not available
# Multiple enhancements to model manager REACT API
1. add a `/sync` route for synchronizing the in-memory model lists to
models.yaml, the models directory, and the autoimport directories.
2. added optional destination directories to convert_model and
merge_model operations.
3. added a `/ckpt_confs` route for retrieving known legacy checkpoint
configuration files.
4. added a `/search` route for finding all models in a directory located
in the server filesystem
5. added a `/add` route for manual addition of a local models
6. added a `/rename` route for renaming and/or rebasing models
7. changed the path of the `import_model` route to `/import`
# Slightly annoying detail:
When adding a model manually using `/add`, the body JSON must exactly
match one of the model configurations returned by `list_models` (i.e.
there is no defaulting of fields). This includes the `error` field,
which should be set to "null".
1. add a /sync route for synchronizing the in-memory model lists to
models.yaml, the models directory, and the autoimport directories.
2. add optional destination_directories to convert_model and merge_model
operations.
3. add /ckpt_confs route for retrieving known legacy checkpoint configuration
files.
4. add /search route for finding all models in a directory located in the server
filesystem
DONE:
- Restore Update Model functionality
- Restore Delete Model functionality
- Restore Model Convert functionality
- Restore Model Merge functionality
- Refine UX (fine tweaks when everything is done - TODO)
TODO
- Add Model (will be finished in a future PR once the backend work is
done)
IAIMantineSelect and IAIMantineMultiSelect have a bit of extra logic that prevents simple select functionality from working as expected.
- extract the styles into hooks
- rename those two components to IAIMantineSearchableSelect and IAIMantineSearchableMultiSelect
- Create IAIMantineSelect (which is just a dropdown) and use it in model manager and a few other places
When we only have a few options to present and searching is not efficient, we should use this instead.
Image files are immutable and we expect deletion to result in no further
requests for a given image, so we can set the max-age to something
thicc.
Resolves#3426
@ebr @brandonrising @maryhipp
- simplify UI logic in `ModelManagerPanel` components
- fix up the types a bit to make it easier to select models
- remove `openModel` state, just make it a useState since it is very local to model manager
similar to the previous commit, update the node editor to not just store models as strings - instead, store the model object.
the model select components in nodes are now just kinda copy-pastes over the linear UI versions of the same components, but they were different enough that we can't just share them.
i explored adding some props to override the linear ui components' logic, but it was too brittle. so just copy/paste.
We were storing all types of models by their model ID, which is a format like `sd-1/main/deliberate`.
This meant we had to do a lot of extra parsing, because nodes actually wants something like `{base_model: 'sd-1', model_name: 'deliberate'}`.
Some of this parsing was done with zod's error-throwing `parse()` method, and in other places it was done with brittle string parsing.
This commit refactors the state to use the object form of models.
There is still a bit of string parsing done in the to construct the ID from the object form, but it's far less complicated.
Also, the zod parsing is now done using `safeParse()`, which does not throw. This requires a few more conditional checks, but should prevent further crashes.
* feat(ui): salvaged gallery UI enhancements
* restore boardimage functionality, load boardimages and remove some cachine optimizations in the name of data integrity
* fix assets, fix load more params
* jk NOW fix assets, fix load more params
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: Mary Hipp Rogers <maryhipp@gmail.com>
- available infill methods is server state - remove it from client state, use the query to populate the dropdown
- add listener to ensure the selected infill method is an available one
As it said in comment to this branch we want to use conditioning run:
```python
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
```
But in code used unconditioning
embeddings(`conditioning_data.unconditioned_embeddings`).
Later in code confirms that we want to run conditioning generation by
comment and tensor concatenation order(as all code expect to get [uc, c]
tensor):
```python
if cfg_injection:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples]
mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample])
```
Adds a Clear Nodes Button with Confirmation Dialog, I think I Did it
right 😃
I am sure there is a way to make the Confirmation look better and have
Yes/No instead of OK/Cancel
- Restore recall functionality to `CurrentImageButtons` and `ImageContextMenu`.
- Debounce metadata requests for `ImageMetadataViewer` and `CurrentImageButtons` by 500ms. It's possible to scroll through these really fast, so we want to debounce the network requests.
- `ImageContextMenu` is lazy-mounted so it does not need to be debounced; it makes the metadata request as soon as you click it.
- Move next/prev image selection logic into hook and add the hotkeys for this to `CurrentImageButtons`. The hotkeys now work when metadata viewer is open.
I will follow up with improved loading state during the debounced calls in the future
- Update for new routes
- Update model storage in state to be `MainModelField` type instead of `string`, simplifies a lot of model handling
- Update model-related stuff for model `name` --> `model_name`
- Update linear graphs to use `MetadataAccumulator`
- Update `ImageMetadataViewer` UI
- Ensure all `recall` functions work (well, the ones that are active anyways)
Metadata for the Linear UI is now sneakily provided via a `MetadataAccumulator` node, which the client populates / hooks up while building the graph.
Additionally, we provide the unexpanded graph with the metadata API response.
Both of these are embedded into the PNGs.
- Remove `metadata` from `ImageDTO`
- Split up the `images/` routes to accomodate this; metadata is only retrieved per-image
- `images/{image_name}` now gets the DTO
- `images/{image_name}/metadata` gets the new metadata
- `images/{image_name}/full` gets the full-sized image file
- Remove old metadata service
- Add `MetadataAccumulator` node, `CoreMetadataField`, hook up to `LatentsToImage` node
- Add `get_raw()` method to `ItemStorage`, retrieves the row from DB as a string, no pydantic parsing
- Update `images`related services to handle storing and retrieving the new metadata
- Add `get_metadata_graph_from_raw_session` which extracts the `graph` from `session` without needing to hydrate the session in pydantic, in preparation for providing it as metadata; also removes all references to the `MetadataAccumulator` node
Our model fields use `model_name`, but the API response uses `name`. Some places use `model_type` but the API response used `type`.
Changed the API response to provide `model_name` and `model_type`, which simplifies how we manage models on the client substantially.
- rewrite Dockerfile
- add a stage to build the UI
- add docker-compose.yml
- add docker-entrypoint.sh such that any command may be used at runtime
- docker-compose adds .env support - add a sample .env file
* fix the test of the config system
* Add torchmetrics==0.11.4 to installer
- Closes#3700
- Closes#3658
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
To be consistent with max_cache_size, the amount of memory to hold in
VRAM for model caching is now controlled by the max_vram_cache_size
configuration parameter.
[feat(ui): memoize ImageContextMenu
selector](265996d230)
Without the selector itself being memoized, the gallery was rerendering
on every progress image.
[feat(ui): memoize NextPrevImageButtons
component](a7b8109ac2)
This was rerendering on every progress image, now it doesn't
[fix(ui): correctly set disabled on invoke button during
generation](1c45d18e6d)
It wasn't disabled when it should have been, looked clickable during
generation.
[fix(nodes): remove board_id column from images
table](00e26ffa9a)
This is extraneous; the `board_images` table holds image-board
relationships. @maryhipp
Image files are immutable and we expect deletion to result in no further requests for a given image, so we can set the max-age to something thicc.
Resolves#3426
Just a small thing now, as nodes are all still wip, but since
@psychedelicious was nice enough to add the progress image node for me,
what I noticed was missing now is the cancel button on nodes tab
@psychedelicious @blessedcoolant Somehow i deleted the branch the other
version of this pull request was on. 🤭
Just an idea, if you think its worth while please make changes ( I did
what I could)
I added a load more to the right arrow to avoid having to open gallery
to load more images,
I am not sure about the icon i used, maybe it should just be the normal
arrow, so you don't even need to show its loading more images.
there is an issue with it not disappearing once all images have been
loaded, (I did play around for a while to try and fix that)
Some users want the model select to take full width coz their model
names might be long. As this is a more frequently used feature,
rearrange it to do that.
Followed by VAE (as it is related to the model) and the Sampler next to
it.
I made a recent change to the function that finds the default root
directory locatoin that broke it when run under Conda (where VIRTUAL_ENV
is not set). This revision fixes the issue.
Mantine's multiselect does not let you edit the search box with mouse, paste into it, etc. Normal select is fine.
I can't remember why I made Lora etc multiselects, but everything seems to work with normal selects, so I've change to that.
- `isLoading` - now `true` *only* on first load
- added `isFetching` - `true` whenever gallery images are fetching
- on first load, show a spinner instead of skeletons. this prevents an awkward flash of skeletons into empty gallery when the gallery doesn't have enough images to fill it.
- removed `imageCategoriesChanged` listener, bc now on app start, both images and assets will be populated. leaving this in caused jank flashes of skeletons when switching gallery tabs when gallery doesn't have images to load
taking the coward's way out on this and just fetching 100 images & 100 assets on app start...
- add `appStarted` action, dispatched once on mount in App.tsx. listener fetches 100 images & 100 assets
- fix bug with selectedBoardId & assets tab
The shift key listener didn't catch pressed when focused in a textarea
or input field, causing jank on slider number inputs.
Add keydown and keyup listeners to all such fields, which ensures that
the `shift` state is always correct.
Also add the action tracking it to `actionsDenylist` to not clutter up
devtools.
The shift key listener didn't catch pressed when focused in a textarea or input field, causing jank on slider number inputs.
Add keydown and keyup listeners to all such fields, which ensures that the `shift` state is always correct.
Also add the action tracking it to `actionsDenylist` to not clutter up devtools.
There was a props on IAISlider to make the input component readonly - I
didn't know this existed and at some point used a component with that
prop as a template for other sliders, copying the flag over.
It's not actually used anywhere, so I removed the prop entirely,
enabling the number inputs everywhere.
There was a props on IAISlider to make the input component readonly - I didn't know this existed and at some point used a component with that prop as a template for other sliders, copying the flag over.
It's not actually used anywhere, so I removed the prop entirely, enabling the number inputs everywhere.
I'm not sure if this was just my local install, but even after a fresh
`yarn install` my upload network request was failing because no file was
passed in. I don't think the `bodySerializer` part is getting run
I'm not sure if this was just my local install, but even after a fresh
`yarn install` my upload network request was failing because no file was
passed in. I don't think the `bodySerializer` part is getting run
This PR is to allow FP16 precision to work on Macs with MPS. In
addition, it centralizes the torch fixes/workarounds required for MPS
into a new backend utility `mps_fixes.py`. This is conditionally
imported in `api_app.py`/`cli_app.py`.
Many MANY thanks to @StAlKeR7779 for patiently working to debug and fix
these issues.
- No longer fail root directory probing if invokeai.yaml is missing
(test is now whether a `models/core` directory exists).
- Migrate script does not overwrite previously-installed models.
- Can run migrate script on an existing 2.3 version directory
with --from and --to pointing to same 2.3 root.
Clip Skip breaks when you supply a number greater than the number of
layers for the model type. So capping this out based on the model on the
frontend
- `sd-1` at 12
- `sd-2` at 24
- Will update later to whatever SDXL needs if it is different.
- Also fixes LoRA's breaking with Clip Skip.
My PR to fix an issue with the handling of formdata in `openapi-fetch` is released. This means we no longer need to patch the package (no patches at all now!).
This PR bumps its version and adds a transformer to our typegen script to handle typing binary form fields correctly as `Blob`.
Also regens types.
This is PR adds the following API methods for managing models:
* list_models (GET)
* update_model (PATCH)
* import_model (POST)
* delete_model (DELETE)
* convert_model (PUT)
* merge_models (PUT)
* load images on gallery render
* wait for models to be loaded before you can invoke
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
This PR enables model manager importation of diffusers-style .bin LoRAs.
However, since there is no backend support for this type of LoRA yet,
attempts to use them will result in an unimplemented error.
It closes#3636 and #3637
The list models route should just be the base route path, and should use query parameters as opposed to path parameters (which cannot be optional)
Removed defaults for update model route - for the purposes of the API, we should always be explicit with this
This PR fixes the migrate script so that it uses the same directory for
both the tokenizer and text encoder CLIP models. This will fix a crash
that occurred during checkpoint->diffusers conversions
This PR also removes the check for an existing models directory in the
target root directory when `invokeai-migrate3` is run.
* close modal when user clicks cancel
* close modal when delete image context cleared
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
A user discovered that 2.3 models whose symbolic names contain the "/"
character are not imported properly by the `migrate-models-3` script.
This fixes the issue by changing "/" to underscore at import time.
- Accordions now may be opened or closed regardless of whether or not
their contents are enabled or active
- Accordions have a short text indicator alerting the user if their
contents are enabled, either a simple `Enabled` or, for accordions like
LoRA or ControlNet, `X Active` if any are active
https://github.com/invoke-ai/InvokeAI/assets/4822129/43db63bd-7ef3-43f2-8dad-59fc7200af2e
- Accordions now may be opened or closed regardless of whether or not their contents are enabled or active
- Accordions have a short text indicator alerting the user if their contents are enabled, either a simple `Enabled` or, for accordions like LoRA or ControlNet, `X Active` if any are active
This caused a lot of re-rendering whenever the selection changed, which caused a huge performance hit. It also made changing the current image lag a bit.
Instead of providing an array of image names as a multi-select dnd payload, there is now no multi-select dnd payload at all - instead, the payload types are used by the `imageDropped` listener to pull the selection out of redux.
Now, the only big re-renders are when the selectionCount changes. In the future I'll figure out a good way to do image names as payload without incurring re-renders.
Every `GalleryImage` was rerendering any time the app rerendered bc the selector function itself was not memoized. This resulted in the memoization cache inside the selector constantly being reset.
Same for `BatchImage`.
Also updated memoization for a few other selectors.
Eg `useGetMainModelsQuery()`, `useGetLoRAModelsQuery()` instead of `useListModelsQuery({base_type})`.
Add specific adapters for each model type. Just more organised and easier to consume models now.
Also updated LoRA UI to use the model name.
This PR is to allow FP16 precision to work on Macs with MPS. In addition, it centralizes the torch fixes/workarounds
required for MPS into a new backend utility file `mps_fixes.py`. This is conditionally imported in `api_app.py`/`cli_app.py`.
Many MANY thanks to StAlKeR7779 for patiently working to debug and fix these issues.
This PR is for adjusting the unit tests in the `tests` directory so that
they no longer throw errors.
I've removed two tests that were obsoleted by the shift to latent nodes,
but `test_graph_execution_state.py` and `test_invoker.py` are throwing
this validation error:
```
TypeError: InvocationServices.__init__() missing 2 required positional arguments: 'boards' and 'board_images'
```
The `invokeai-configure` script migrates the old invokeai.init file to
the new invokeai.yaml format. However, the parser for the invokeai.init
file was missing the names of the k* samplers and was giving a parser
error on any invokeai.init file that referred to one of these samplers.
This PR fixes the problem.
Ironically, there is no longer the concept of the preferred scheduler in
3.0, and so these sampler names are simply ignored and not written into
`invokeai.yaml`
This introduces the core functionality for batch operations on images and multiple selection in the gallery/batch manager.
A number of other substantial changes are included:
- `imagesSlice` is consolidated into `gallerySlice`, allowing for simpler selection of filtered images
- `batchSlice` is added to manage the batch
- The wonky context pattern for image deletion has been changed, much simpler now using a `imageDeletionSlice` and redux listeners; this needs to be implemented still for the other image modals
- Minimum gallery size in px implemented as a hook
- Many style fixes & several bug fixes
TODO:
- The UI and UX need to be figured out, especially for controlnet
- Batch processing is not hooked up; generation does not do anything with batch
- Routes to support batch image operations, specifically delete and add/remove to/from boards
@blessedcoolant it looks like with the new theme buttons not being
transparent the progress bar was completely hidden, I moved to be on
top, however it was not transparent so it hid the invoke text, after
trying for a while couldn't get it to be transparent, so I just made the
height 15%,
- Set min size for floating gallery panel
- Correct the default pinned width (it cannot be less than the min width
and this was sometimes happening during window resize)
- Set min size for floating gallery panel
- Correct the default pinned width (it cannot be less than the min width and this was sometimes happening during window resize)
Add `useMinimumPanelSize()` hook to provide minimum resizable panel sizes (in pixels).
The library we are using for the gallery panel uses percentages only. To provide a minimum size in pixels, we need to do some math to calculate the percentage of window size that corresponds to the desired min width in pixels.
The node polyfills needed to run the `swagger-parser` library (used to
dereference the OpenAPI schema) cause the canvas tab to immediately
crash when the package build is used in another react application.
I'm sure this is fixable but it's not clear what is causing the issue
and troubleshooting is very time consuming.
Selectively rolling back the implementation of `swagger-parser`.
The node polyfills needed to run the `swagger-parser` library (used to dereference the OpenAPI schema) cause the canvas tab to immediately crash when the package build is used in another react application.
I'm sure this is fixable but it's not clear what is causing the issue and troubleshooting is very time consuming.
Selectively rolling back the implementation of `swagger-parser`.
[feat(ui): remove themes, add hand-crafted dark and light
modes](032c7e68d0)
[032c7e6](032c7e68d0)
Themes are very fun but due to the differences in perceived saturation
and lightness across the
the color spectrum, it's impossible to have have multiple themes that
look great without hand-
crafting *every* shade for *every* theme. We've ended up with 4 OK
themes (well, 3, because the
light theme was pretty bad).
I've removed the themes and added color mode support. There is now a
single dark and light mode,
each with their own color palette and the classic grey / purple / yellow
invoke colors that
@blessedcoolant first designed.
I've re-styled almost everything except the model manager and lightbox,
which I keep forgetting
to work on.
One new concept is the Chakra `layerStyle`. This lets us define "layers"
- think body, first layer,
second layer, etc - that can be applied on various components. By
defining layers, we can be more
consistent about the z-axis and its relationship to color and lightness.
Themes are very fun but due to the differences in perceived saturation and lightness across the
the color spectrum, it's impossible to have have multiple themes that look great without hand-
crafting *every* shade for *every* theme. We've ended up with 4 OK themes (well, 3, because the
light theme was pretty bad).
I've removed the themes and added color mode support. There is now a single dark and light mode,
each with their own color palette and the classic grey / purple / yellow invoke colors that
@blessedcoolant first designed.
I've re-styled almost everything except the model manager and lightbox, which I keep forgetting
to work on.
One new concept is the Chakra `layerStyle`. This lets us define "layers" - think body, first layer,
second layer, etc - that can be applied on various components. By defining layers, we can be more
consistent about the z-axis and its relationship to color and lightness.
The TS Language Server slows down immensely with our translation JSON, which is used to provide kinda-type-safe translation keys. I say "kinda", because you don't get autocomplete - you only get red squigglies when the key is incorrect.
To improve the performance, we can opt out of this process entirely, at the cost of no red squigglies for translation keys. Hopefully we can resolve this in the future.
It's not clear why this became an issue only recently (like past couple weeks). We've tried rolling back the app dependencies, VSCode extensions, VSCode itself, and the TS version to before the time when the issue started, but nothing seems to improve the performance.
1. Disable `resolveJsonModule` in `tsconfig.json`
2. Ignore TS in `i18n.ts` when importing the JSON
3. Comment out the custom types in `i18.d.ts` entirely
It's possible that only `3` is needed to fix the issue.
I've tested building the app and running the build - it works fine, and translation works fine.
Rewrite lora to be applied by model patching as it gives us benefits:
1) On model execution calculates result only on model weight, while with
hooks we need to calculate on model and each lora
2) As lora now patched in model weights, there no need to store lora in
vram
Results:
Speed:
| loras count | hook | patch |
| --- | --- | --- |
| 0 | ~4.92 it/s | ~4.92 it/s |
| 1 | ~3.51 it/s | ~4.89 it/s |
| 2 | ~2.76 it/s | ~4.92 it/s |
VRAM:
| loras count | hook | patch |
| --- | --- | --- |
| 0 | ~3.6 gb | ~3.6 gb |
| 1 | ~4.0 gb | ~3.6 gb |
| 2 | ~4.4 gb | ~3.7 gb |
As based on #3547 wait to merge.
# Restore invokeai-configure and invokeai-model-install
This PR updates invokeai-configure and invokeai-model-install to work
with the new model manager file layout. It addresses a naming issue for
`ModelType.Main` (was `ModelType.Pipeline`) requested by
@blessedcoolant, and adds back the feature that allows users to dump
models into an `autoimport` directory for discovery at startup time.
Trying to get a few ControlNet extras in before 3.0 release:
- SegmentAnything ControlNet preprocessor node
- LeResDepth ControlNet preprocessor node (but commented out till
controlnet_aux v0.0.6 is released & required by InvokeAI)
- TileResampler ControlNet preprocessor node (should be equivalent to
Mikubill/sd-webui-controlnet extension tile_resampler)
- fix for Midas ControlNet preprocessor error with images that have
alpha channel
Example usage of SegmentAnything preprocessor node:
The installer TUI requires a minimum window width and height to provide
a satisfactory user experience. If, after trying and exhausting all
means of enlarging the window (on Linux, Mac and Windows) the window is
still too small, this PR generates a message telling the user to enlarge
the window and pausing until they do so. If the user fails to enlarge
the window the program will proceed, and either issue an error message
that it can't continue (on Windows), or show a clipped display that the
user can remedy by enlarging the window.
"Fixes" the test suite generally so it doesn't fail CI, but some tests
needed to be skipped/xfailed due to recent refactor.
- ignore three test suites that broke following the model manager
refactor
- move `InvocationServices` fixture to `conftest.py`
- add `boards` items to the `InvocationServices` fixture
This PR makes the unit tests work, but end-to-end tests are temporarily
commented out due to `invokeai-configure` being broken in `main` -
pending #3547
Looks like a lot of the tests need to be rewritten as they reference
`TextToImageInvocation` / `ImageToImageInvocation`
fixes the test suite generally, but some tests needed to be
skipped/xfailed due to recent refactor
- ignore three test suites that broke following the model manager
refactor
- move InvocationServices fixture to conftest.py
- add `boards` InvocationServices to the fixture
This PR adds the "control_mode" option to ControlNet implementation.
Possible control_mode options are:
- balanced -- this is the default, same as previous implementation
without control_mode
- more_prompt -- pays more attention to the prompt
- more _control -- pays more attention to the ControlNet (in earlier
implementations this was called "guess_mode")
- unbalanced -- pays even more attention to the ControlNet
balanced, more_prompt, and more_control should be nearly identical to
the equivalent options in the [auto1111 sd-webui-controlnet
extension](https://github.com/Mikubill/sd-webui-controlnet#more-control-modes-previously-called-guess-mode)
The changes to enable balanced, more_prompt, and more_control are
managed deeper in the code by two booleans, "soft_injection" and
"cfg_injection". The three control mode options in sd-webui-controlnet
map to these booleans like:
!soft_injection && !cfg_injection ⇒ BALANCED
soft_injection && cfg_injection ⇒ MORE_CONTROL
soft_injection && !cfg_injection ⇒ MORE_PROMPT
The "unbalanced" option simply exposes the fourth possible combination
of these two booleans:
!soft_injection && cfg_injection ⇒ UNBALANCED
With "unbalanced" mode it is very easy to overdrive the controlnet
inputs. It's recommended to use a cfg_scale between 2 and 4 to mitigate
this, along with lowering controlnet weight and possibly lowering "end
step percent". With those caveats, "unbalanced" can yield interesting
results.
Example of all four modes using Canny edge detection ControlNet with
prompt "old man", identical params except for control_mode:
Top middle: BALANCED
Top right: MORE_CONTROL
Bottom middle: MORE_PROMPT
Bottom right : UNBALANCED
I kind of chose this seed because it shows pretty rough results with
BALANCED (the default), but in my opinion better results with both
MORE_CONTROL and MORE_PROMPT. And you can definitely see how MORE_PROMPT
pays more attention to the prompt, and MORE_CONTROL pays more attention
to the control image. And shows that UNBALANCED with default cfg_scale
etc is unusable.
But here are four examples from same series (same seed etc), all have
control_mode = UNBALANCED but now cfg_scale is set to 3.
And param differences are:
Top middle: prompt="old man", control_weight=0.3, end_step_percent=0.5
Top right: prompt="old man", control_weight=0.4, end_step_percent=1.0
Bottom middle: prompt=None, control_weight=0.3, end_step_percent=0.5
Bottom right: prompt=None, control_weight=0.4, end_step_percent=1.0
So with the right settings UNBALANCED seems useful.
Everything seems to be working.
- Due to a change to `reactflow`, I regenerated `yarn.lock`
- New chakra CLI fixes issue I had made a patch for; removed the patch
- Change to fontsource changed how we import that font
- Change to fontawesome means we lost the txt2img tab icon, just chose a
similar one
Everything seems to be working.
- Due to a change to `reactflow`, I regenerated `yarn.lock`
- New chakra CLI fixes issue I had made a patch for; removed the patch
- Change to fontsource changed how we import that font
- Change to fontawesome means we lost the txt2img tab icon, just chose a similar one
Only "real" conflicts were in:
invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
- Reset and Upload buttons along top of initial image
- Also had to mess around with the control net & DnD image stuff after changing the styles
- Abstract image upload logic into hook - does not handle native HTML drag and drop upload - only the button click upload
`openapi-fetch` does not handle non-JSON `body`s, always stringifying them, and sets the `content-type` to `application/json`.
The patch here does two things:
- Do not stringify `body` if it is one of the types that should not be stringified (https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API/Using_Fetch#body)
- Do not add `content-type: application/json` unless it really is stringified JSON.
Upstream issue: https://github.com/drwpow/openapi-typescript/issues/1123
I'm not a bit lost on fixing the types and adding tests, so not raising a PR upstream.
*migrate from `openapi-typescript-codegen` to `openapi-typescript` and `openapi-fetch`*
`openapi-typescript-codegen` is not very actively maintained - it's been over a year since the last update.
`openapi-typescript` and `openapi-fetch` are part of the actively maintained repo. key differences:
- provides a `fetch` client instead of `axios`, which means we need to be a bit more verbose with typing thunks
- fetch client is created at runtime and has a very nice typescript DX
- generates a single file with all types in it, from which we then extract individual types. i don't like how verbose this is, but i do like how it is more explicit.
- removed npm api generation scripts - now we have a single `typegen` script
overall i have more confidence in this new library.
*use nanostores for api base and token*
very simple reactive store for api base url and token. this was suggested in the `openapi-fetch` docs and i quite like the strategy.
*organise rtk-query api*
split out each endpoint (models, images, boards, boardImages) into their own api extensions. tidy!
Unsure at which moment it broke, but now I can't convert vae(and model
as vae it's part) without this fix.
Need further research - maybe it's breaking change in `transformers`?
Changes:
* Linux `install.sh` now prints the maximum python version to use in
case no installed python version matches
Commits:
fix(linux): installer script prints maximum python version usable
Basically updated all slices to be more descriptive in their names. Did so in order to make sure theres good naming scheme available for secondary models.
Update the text to imaeg and image to image graphs to work with the new model loader. Currently only supports 1.x models. Will update this soon to make it work with all models.
label:Is there an existing issue for this problem?
description:|
Please use the [search function](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
irst to see if an issue already exists for the bug you encountered.
Please [search](https://github.com/invoke-ai/InvokeAI/issues) first to see if an issue already exists for the problem.
options:
- label:I have searched the existing issues
required:true
@ -33,80 +28,119 @@ body:
- type:dropdown
id:os_dropdown
attributes:
label:OS
description:Which operating System did you use when the bug occured
label:Operating system
description:Your computer's operating system.
multiple:false
options:
- 'Linux'
- 'Windows'
- 'macOS'
- 'other'
validations:
required:true
- type:dropdown
id:gpu_dropdown
attributes:
label:GPU
description:Which kind of Graphic-Adapter is your System using
label:GPU vendor
description:Your GPU's vendor.
multiple:false
options:
- 'cuda'
- 'amd'
- 'mps'
- 'cpu'
- 'Nvidia (CUDA)'
- 'AMD (ROCm)'
- 'Apple Silicon (MPS)'
- 'None (CPU)'
validations:
required:true
- type:input
id:gpu_model
attributes:
label:GPU model
description:Your GPU's model. If on Apple Silicon, this is your Mac's chip. Leave blank if on CPU.
placeholder:ex. RTX 2080 Ti, Mac M1 Pro
validations:
required:false
- type:input
id:vram
attributes:
label:VRAM
description:Size of the VRAM if known
label:GPU VRAM
description:Your GPU's VRAM. If on Apple Silicon, this is your Mac's unified memory. Leave blank if on CPU.
placeholder:8GB
validations:
required:false
- type:input
id:version-number
attributes:
label:What version did you experience this issue on?
label:Version number
description:|
Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
placeholder:X.X.X
The version of Invoke you have installed. If it is not the latest version, please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
placeholder:ex. 3.6.1
validations:
required:true
- type:input
id:browser-version
attributes:
label:Browser
description:Your web browser and version.
placeholder:ex. Firefox 123.0b3
validations:
required:true
- type:textarea
id:python-deps
attributes:
label:Python dependencies
description:|
If the problem occurred during image generation, click the gear icon at the bottom left corner, click "About", click the copy button and then paste here.
validations:
required:false
- type:textarea
id:what-happened
attributes:
label:What happened?
label:What happened
description:|
Briefly describe what happened, what you expected to happen and how to reproduce this bug.
placeholder:When using the webinterface and right-clicking on button X instead of the popup-menu there error Y appears
Describe what happened. Include any relevant error messages, stack traces and screenshots here.
placeholder:I clicked button X and then Y happened.
validations:
required:true
- type:textarea
id:what-you-expected
attributes:
label:Screenshots
description:If applicable, add screenshots to help explain your problem
placeholder:this is what the result looked like <screenshot>
label:What you expected to happen
description:Describe what you expected to happen.
placeholder:I expected Z to happen.
validations:
required:true
- type:textarea
id:how-to-repro
attributes:
label:How to reproduce the problem
description:List steps to reproduce the problem.
placeholder:Start the app, generate an image with these settings, then click button X.
validations:
required:false
- type:textarea
id:additional-context
attributes:
label:Additional context
description:Add any other context about the problem here
description:Any other context that might help us to understand the problem.
placeholder:Only happens when there is full moon and Friday the 13th on Christmas Eve 🎅🏻
validations:
required:false
- type:input
id:contact
id:discord-username
attributes:
label:Contact Details
description:__OPTIONAL__ How can we get in touch with you if we need more info (besides this issue)?
description:Commit a idea or Request a new feature
description:Contribute a idea or request a new feature
title: '[enhancement]:'
labels: ['enhancement']
# assignees:
@ -9,14 +9,14 @@ body:
- type:markdown
attributes:
value:|
Thanks for taking the time to fill out this Feature request!
Thanks for taking the time to fill out this feature request!
- type:checkboxes
attributes:
label:Is there an existing issue for this?
description:|
Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
to see if a simmilar issue already exists for the feature you want to request
to see if a similar issue already exists for the feature you want to request
options:
- label:I have searched the existing issues
required:true
@ -34,12 +34,9 @@ body:
id:whatisexpected
attributes:
label:What should this feature add?
description:Please try to explain the functionality this feature should add
description:Explain the functionality this feature should add. Feature requests should be for single features. Please create multiple requests if you want to request multiple features.
placeholder:|
Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
Great benefits with automatic labeling, assigning and other functionalitys not available in that form
via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
I'd like a button that creates an image of banana sushi every time I press it. Each image should be different. There should be a toggle next to the button that enables strawberry mode, in which the images are of strawberry sushi instead.
validations:
required:true
@ -51,6 +48,6 @@ body:
- type:textarea
attributes:
label:Aditional Content
label:Additional Content
description:Add any other context or screenshots about the feature request here.
placeholder:This is a Mockup of the design how I imagine it <screenshot>
placeholder:This is a mockup of the design how I imagine it <screenshot>
<!--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_
[![discord badge]][discord link] [![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link] [![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link] [![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
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</div>
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
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.
| **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) |
| [Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs] |
</div>
## Quick Start
1. Download and unzip the installer from the bottom of the [latest release][latest release link].
2. Run the installer script.
- **Windows**: Double-click on the `install.bat` script.
- **macOS**: Open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press enter.
- **Linux**: Run `install.sh`.
3. When prompted, enter a location for the install and select your GPU type.
4. Once the install finishes, find the directory you selected during install. The default location is `C:\Users\Username\invokeai` for Windows or `~/invokeai` for Linux/macOS.
5. Run the launcher script (`invoke.bat` for Windows, `invoke.sh` for macOS and Linux) the same way you ran the installer script in step 2.
6. Select option 1 to start the application. Once it starts up, open your browser and go to <http://localhost:9090>.
7. Open the model manager tab to install a starter model and then you'll be ready to generate.
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.
For more help, please join our [Discord][discord link].
## Features
Full details on features can be found in [our documentation][features docs].
### Web Server & UI
Invoke runs a locally hosted web server & React UI with an industry-leading user experience.
### Unified Canvas
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/out-painting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
### Workflows & Nodes
Invoke offers a fully featured workflow management solution, enabling users to combine the power of node-based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
### Board & Gallery Management
Invoke features an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
### Other features
- Support for both ckpt and diffusers models
- SD1.5, SD2.0, and SDXL support
- Upscaling Tools
- Embedding Manager & Support
- Model Manager & Support
- Workflow creation & management
- Node-Based Architecture
## Contributing
Anyone who wishes to contribute to this project - whether documentation, features, bug fixes, code cleanup, testing, or code reviews - is very much encouraged to do so.
Get started with contributing by reading our [contribution documentation][contributing docs], joining the [#dev-chat] or the GitHub discussion board.
We hope you enjoy using Invoke as much as we enjoy creating it, and we hope you will elect to become part of our community.
## Thanks
Invoke is a combined effort of [passionate and talented people from across the world][contributors]. We thank them for their time, hard work and effort.
[latest commit to main badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/main?icon=github&color=yellow&label=last%20dev%20commit&cache=900
[latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
[translation status link]: https://hosted.weblate.org/engage/invokeai/
</div>
_**Note: The UI is not fully functional on `main`. If you need a stable UI based on `main`, use the `pre-nodes` tag while we [migrate to a new backend](https://github.com/invoke-ai/InvokeAI/discussions/3246).**_
InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.
**Quick links**: [[How to Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<ahref="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<ahref="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<ahref="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<ahref="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<ahref="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
_Note: InvokeAI is rapidly evolving. Please use the
[Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
requests. Be sure to use the provided templates. They will help us diagnose issues faster._
## FOR DEVELOPERS - MIGRATING TO THE 3.0.0 MODELS FORMAT
The models directory and models.yaml have changed. To migrate to the
new layout, please follow this recipe:
1. Run `python scripts/migrate_models_to_3.0.py <path_to_root_directory>
2. This will create a new models directory named `models-3.0` and a
new config directory named `models.yaml-3.0`, both in the current
working directory. If you prefer to name them something else, pass
the `--dest-directory` and/or `--dest-yaml` arguments.
3. Check that the new models directory and yaml file look ok.
4. Replace the existing directory and file, keeping backup copies just in
InvokeAI is supported across Linux, Windows and macOS. Linux
users can use either an Nvidia-based card (with CUDA support) or an
AMD card (using the ROCm driver).
### System
You will need one of the following:
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
- An Apple computer with an M1 chip.
- An AMD-based graphics card with 4GB or more VRAM memory. (Linux only)
We do not recommend the GTX 1650 or 1660 series video cards. They are
unable to run in half-precision mode and do not have sufficient VRAM
to render 512x512 images.
### Memory
- At least 12 GB Main Memory RAM.
### Disk
- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
## Features
Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/)
### *Web Server & UI*
InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server.
### *Unified Canvas*
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
### *Advanced Prompt Syntax*
InvokeAI's advanced prompt syntax allows for token weighting, cross-attention control, and prompt blending, allowing for fine-tuned tweaking of your invocations and exploration of the latent space.
### *Command Line Interface*
For users utilizing a terminal-based environment, or who want to take advantage of CLI features, InvokeAI offers an extensive and actively supported command-line interface that provides the full suite of generation functionality available in the tool.
### Other features
- *Support for both ckpt and diffusers models*
- *SD 2.0, 2.1 support*
- *Noise Control & Tresholding*
- *Popular Sampler Support*
- *Upscaling & Face Restoration Tools*
- *Embedding Manager & Support*
- *Model Manager & Support*
### Coming Soon
- *Node-Based Architecture & UI*
- And more...
### Latest Changes
For our latest changes, view our [Release
Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
[CHANGELOG](docs/CHANGELOG.md).
## Troubleshooting
Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
problems and other issues.
## Contributing
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
cleanup, testing, or code reviews, is very much encouraged to do so.
To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
If you'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
If you are unfamiliar with how
to contribute to GitHub projects, here is a
[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**.
We hope you enjoy using our software as much as we enjoy creating it,
and we hope that some of those of you who are reading this will elect
to become part of our community.
Welcome to InvokeAI!
### Contributors
This fork is a combined effort of various people from across the world.
[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for
their time, hard work and effort.
Thanks to [Weblate](https://weblate.org/) for generously providing translation services to this project.
### Support
For support, please use this repository's GitHub Issues tracking service, or join the Discord.
Original portions of the software are Copyright (c) 2023 by respective contributors.
- 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.
## Quickstart
No `docker compose`, no persistence, single command, using the official images:
**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 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 probably won't work. Update to a recent version.
3. Ensure docker daemon is able to access the GPU.
> 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.
### 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 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`. Navigate to the Model Manager tab and install some models before generating.
### Use a GPU
- Linux is *recommended* for GPU support in Docker.
- 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/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 before running `./run.sh`.
## Customize
Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `run.sh`, your custom values will be used.
You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The default is `~/invokeai`. Example:
```bash
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000
GPU_DRIVER=cuda
```
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.
Stable Diffusion distribution by InvokeAI: https://github.com/invoke-ai
The Docker image tracks the `main` branch of the InvokeAI project, which means it includes the latest features, but may contain some bugs.
Your working directory is mounted under the `/workspace` path inside the pod. The models are in `/workspace/invokeai/models`, and outputs are in `/workspace/invokeai/outputs`.
> **Only the /workspace directory will persist between pod restarts!**
> **If you _terminate_ (not just _stop_) the pod, the /workspace will be lost.**
## Quickstart
1. Launch a pod from this template. **It will take about 5-10 minutes to run through the initial setup**. Be patient.
1. Wait for the application to load.
- TIP: you know it's ready when the CPU usage goes idle
- You can also check the logs for a line that says "_Point your browser at..._"
1. Open the Invoke AI web UI: click the `Connect` => `connect over HTTP` button.
1. Generate some art!
## Other things you can do
At any point you may edit the pod configuration and set an arbitrary Docker command. For example, you could run a command to downloads some models using `curl`, or fetch some images and place them into your outputs to continue a working session.
If you need to run *multiple commands*, define them in the Docker Command field like this:
This image includes a couple of handy tools to help you get the data into the pod (such as your custom models or embeddings), and out of the pod (such as downloading your outputs). Here are your options for getting your data in and out of the pod:
- **SSH server**:
1. Make sure to create and set your Public Key in the RunPod settings (follow the official instructions)
1. Add an exposed port 22 (TCP) in the pod settings!
1. When your pod restarts, you will see a new entry in the `Connect` dialog. Use this SSH server to `scp` or `sftp` your files as necessary, or SSH into the pod using the fully fledged SSH server.
1. On your computer, `pip install magic-wormhole` (see above instructions for details)
1. Connect to the command line **using the "light" SSH client** or the browser-based console. _Currently there's a bug where `wormhole` isn't available when connected to "full" SSH server, as described above_.
1.`wormhole send /workspace/invokeai/outputs` will send the entire `outputs` directory. You can also send individual files.
1. Once packaged, you will see a `wormhole receive <123-some-words>` command. Copy it
1. Paste this command into the terminal on your local machine to securely download the payload.
1. It works the same in reverse: you can `wormhole send` some models from your computer to the pod. Again, save your files somewhere in `/workspace` or they will be lost when the pod is stopped.
- **RunPod's Cloud Sync feature** may be used to sync the persistent volume to cloud storage. You could, for example, copy the entire `/workspace` to S3, add some custom models to it, and copy it back from S3 when launching new pod configurations. Follow the Cloud Sync instructions.
### Disable the NSFW checker
The NSFW checker is enabled by default. To disable it, edit the pod configuration and set the following command:
This release (along with the post1 and post2 follow-on releases) expands support for additional LoRA and LyCORIS models, upgrades diffusers versions, and fixes a few bugs.
### LoRA and LyCORIS Support Improvement
A number of LoRA/LyCORIS fine-tune files (those which alter the text encoder as well as the unet model) were not having the desired effect in InvokeAI. This bug has now been fixed. Full documentation of LoRA support is available at InvokeAI LoRA Support.
Previously, InvokeAI did not distinguish between LoRA/LyCORIS models based on Stable Diffusion v1.5 vs those based on v2.0 and 2.1, leading to a crash when an incompatible model was loaded. This has now been fixed. In addition, the web pulldown menus for LoRA and Textual Inversion selection have been enhanced to show only those files that are compatible with the currently-selected Stable Diffusion model.
Support for the newer LoKR LyCORIS files has been added.
### Library Updates and Speed/Reproducibility Advancements
The major enhancement in this version is that NVIDIA users no longer need to decide between speed and reproducibility. Previously, if you activated the Xformers library, you would see improvements in speed and memory usage, but multiple images generated with the same seed and other parameters would be slightly different from each other. This is no longer the case. Relative to 2.3.5 you will see improved performance when running without Xformers, and even better performance when Xformers is activated. In both cases, images generated with the same settings will be identical.
Here are the new library versions:
Library Version
Torch 2.0.0
Diffusers 0.16.1
Xformers 0.0.19
Compel 1.1.5
Other Improvements
### Performance Improvements
When a model is loaded for the first time, InvokeAI calculates its checksum for incorporation into the PNG metadata. This process could take up to a minute on network-mounted disks and WSL mounts. This release noticeably speeds up the process.
### Bug Fixes
The "import models from directory" and "import from URL" functionality in the console-based model installer has now been fixed.
When running the WebUI, we have reduced the number of times that InvokeAI reaches out to HuggingFace to fetch the list of embeddable Textual Inversion models. We have also caught and fixed a problem with the updater not correctly detecting when another instance of the updater is running
## v2.3.4 <small>(7 April 2023)</small>
What's New in 2.3.4
This features release adds support for LoRA (Low-Rank Adaptation) and LyCORIS (Lora beYond Conventional) models, as well as some minor bug fixes.
### LoRA and LyCORIS Support
LoRA files contain fine-tuning weights that enable particular styles, subjects or concepts to be applied to generated images. LyCORIS files are an extended variant of LoRA. InvokeAI supports the most common LoRA/LyCORIS format, which ends in the suffix .safetensors. You will find numerous LoRA and LyCORIS models for download at Civitai, and a small but growing number at Hugging Face. Full documentation of LoRA support is available at InvokeAI LoRA Support.( Pre-release note: this page will only be available after release)
To use LoRA/LyCORIS models in InvokeAI:
Download the .safetensors files of your choice and place in /path/to/invokeai/loras. This directory was not present in earlier version of InvokeAI but will be created for you the first time you run the command-line or web client. You can also create the directory manually.
Add withLora(lora-file,weight) to your prompts. The weight is optional and will default to 1.0. A few examples, assuming that a LoRA file named loras/sushi.safetensors is present:
family sitting at dinner table eating sushi withLora(sushi,0.9)
family sitting at dinner table eating sushi withLora(sushi, 0.75)
family sitting at dinner table eating sushi withLora(sushi)
Multiple withLora() prompt fragments are allowed. The weight can be arbitrarily large, but the useful range is roughly 0.5 to 1.0. Higher weights make the LoRA's influence stronger. Negative weights are also allowed, which can lead to some interesting effects.
Generate as you usually would! If you find that the image is too "crisp" try reducing the overall CFG value or reducing individual LoRA weights. As is the case with all fine-tunes, you'll get the best results when running the LoRA on top of the model similar to, or identical with, the one that was used during the LoRA's training. Don't try to load a SD 1.x-trained LoRA into a SD 2.x model, and vice versa. This will trigger a non-fatal error message and generation will not proceed.
You can change the location of the loras directory by passing the --lora_directory option to `invokeai.
### New WebUI LoRA and Textual Inversion Buttons
This version adds two new web interface buttons for inserting LoRA and Textual Inversion triggers into the prompt as shown in the screenshot below.
Clicking on one or the other of the buttons will bring up a menu of available LoRA/LyCORIS or Textual Inversion trigger terms. Select a menu item to insert the properly-formatted withLora() or <textual-inversion> prompt fragment into the positive prompt. The number in parentheses indicates the number of trigger terms currently in the prompt. You may click the button again and deselect the LoRA or trigger to remove it from the prompt, or simply edit the prompt directly.
Currently terms are inserted into the positive prompt textbox only. However, some textual inversion embeddings are designed to be used with negative prompts. To move a textual inversion trigger into the negative prompt, simply cut and paste it.
By default the Textual Inversion menu only shows locally installed models found at startup time in /path/to/invokeai/embeddings. However, InvokeAI has the ability to dynamically download and install additional Textual Inversion embeddings from the HuggingFace Concepts Library. You may choose to display the most popular of these (with five or more likes) in the Textual Inversion menu by going to Settings and turning on "Show Textual Inversions from HF Concepts Library." When this option is activated, the locally-installed TI embeddings will be shown first, followed by uninstalled terms from Hugging Face. See The Hugging Face Concepts Library and Importing Textual Inversion files for more information.
### Minor features and fixes
This release changes model switching behavior so that the command-line and Web UIs save the last model used and restore it the next time they are launched. It also improves the behavior of the installer so that the pip utility is kept up to date.
### Known Bugs in 2.3.4
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
## v2.3.3 <small>(28 March 2023)</small>
This is a bugfix and minor feature release.
### Bugfixes
Since version 2.3.2 the following bugs have been fixed:
Bugs
When using legacy checkpoints with an external VAE, the VAE file is now scanned for malware prior to loading. Previously only the main model weights file was scanned.
Textual inversion will select an appropriate batchsize based on whether xformers is active, and will default to xformers enabled if the library is detected.
The batch script log file names have been fixed to be compatible with Windows.
Occasional corruption of the .next_prefix file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
Support loading of legacy config files that have no personalization (textual inversion) section.
An infinite loop when opening the developer's console from within the invoke.sh script has been corrected.
Documentation fixes, including a recipe for detecting and fixing problems with the AMD GPU ROCm driver.
Enhancements
It is now possible to load and run several community-contributed SD-2.0 based models, including the often-requested "Illuminati" model.
The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI embeddings directory.
If no --model is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
On Linux systems, the invoke.sh launcher now uses a prettier console-based interface. To take advantage of it, install the dialog package using your package manager (e.g. sudo apt install dialog).
When loading legacy models (safetensors/ckpt) you can specify a custom config file and/or a VAE by placing like-named files in the same directory as the model following this example:
my-favorite-model.ckpt
my-favorite-model.yaml
my-favorite-model.vae.pt # or my-favorite-model.vae.safetensors
### Known Bugs in 2.3.3
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
## v2.3.2 <small>(11 March 2023)</small>
This is a bugfix and minor feature release.
### Bugfixes
Since version 2.3.1 the following bugs have been fixed:
Black images appearing for potential NSFW images when generating with legacy checkpoint models and both --no-nsfw_checker and --ckpt_convert turned on.
Black images appearing when generating from models fine-tuned on Stable-Diffusion-2-1-base. When importing V2-derived models, you may be asked to select whether the model was derived from a "base" model (512 pixels) or the 768-pixel SD-2.1 model.
The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
When using the model installer console app, models failed to import correctly when importing from directories with spaces in their names. A similar issue with the output directory was also fixed.
Crashes that occurred during model merging.
Restore previous naming of Stable Diffusion base and 768 models.
Upgraded to latest versions of diffusers, transformers, safetensors and accelerate libraries upstream. We hope that this will fix the assertion NDArray > 2**32 issue that MacOS users have had when generating images larger than 768x768 pixels. Please report back.
As part of the upgrade to diffusers, the location of the diffusers-based models has changed from models/diffusers to models/hub. When you launch InvokeAI for the first time, it will prompt you to OK a one-time move. This should be quick and harmless, but if you have modified your models/diffusers directory in some way, for example using symlinks, you may wish to cancel the migration and make appropriate adjustments.
New "Invokeai-batch" script
### Invoke AI Batch
2.3.2 introduces a new command-line only script called invokeai-batch that can be used to generate hundreds of images from prompts and settings that vary systematically. This can be used to try the same prompt across multiple combinations of models, steps, CFG settings and so forth. It also allows you to template prompts and generate a combinatorial list like:
a shack in the mountains, photograph
a shack in the mountains, watercolor
a shack in the mountains, oil painting
a chalet in the mountains, photograph
a chalet in the mountains, watercolor
a chalet in the mountains, oil painting
a shack in the desert, photograph
...
If you have a system with multiple GPUs, or a single GPU with lots of VRAM, you can parallelize generation across the combinatorial set, reducing wait times and using your system's resources efficiently (make sure you have good GPU cooling).
To try invokeai-batch out. Launch the "developer's console" using the invoke launcher script, or activate the invokeai virtual environment manually. From the console, give the command invokeai-batch --help in order to learn how the script works and create your first template file for dynamic prompt generation.
### Known Bugs in 2.3.2
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise a Trojan alert for the codeformer.pth face restoration model. As far as we have been able to determine, this is a false positive and can be safely whitelisted.
## v2.3.1 <small>(22 February 2023)</small>
This is primarily a bugfix release, but it does provide several new features that will improve the user experience.
### Enhanced support for model management
InvokeAI now makes it convenient to add, remove and modify models. You can individually import models that are stored on your local system, scan an entire folder and its subfolders for models and import them automatically, and even directly import models from the internet by providing their download URLs. You also have the option of designating a local folder to scan for new models each time InvokeAI is restarted.
There are three ways of accessing the model management features:
From the WebUI, click on the cube to the right of the model selection menu. This will bring up a form that allows you to import models individually from your local disk or scan a directory for models to import.
Using the Model Installer App
Choose option (5) download and install models from the invoke launcher script to start a new console-based application for model management. You can use this to select from a curated set of starter models, or import checkpoint, safetensors, and diffusers models from a local disk or the internet. The example below shows importing two checkpoint URLs from popular SD sites and a HuggingFace diffusers model using its Repository ID. It also shows how to designate a folder to be scanned at startup time for new models to import.
Command-line users can start this app using the command invokeai-model-install.
Using the Command Line Client (CLI)
The !install_model and !convert_model commands have been enhanced to allow entering of URLs and local directories to scan and import. The first command installs .ckpt and .safetensors files as-is. The second one converts them into the faster diffusers format before installation.
Internally InvokeAI is able to probe the contents of a .ckpt or .safetensors file to distinguish among v1.x, v2.x and inpainting models. This means that you do not need to include "inpaint" in your model names to use an inpainting model. Note that Stable Diffusion v2.x models will be autoconverted into a diffusers model the first time you use it.
Please see INSTALLING MODELS for more information on model management.
### An Improved Installer Experience
The installer now launches a console-based UI for setting and changing commonly-used startup options:
After selecting the desired options, the installer installs several support models needed by InvokeAI's face reconstruction and upscaling features and then launches the interface for selecting and installing models shown earlier. At any time, you can edit the startup options by launching invoke.sh/invoke.bat and entering option (6) change InvokeAI startup options
Command-line users can launch the new configure app using invokeai-configure.
This release also comes with a renewed updater. To do an update without going through a whole reinstallation, launch invoke.sh or invoke.bat and choose option (9) update InvokeAI . This will bring you to a screen that prompts you to update to the latest released version, to the most current development version, or any released or unreleased version you choose by selecting the tag or branch of the desired version.
Command-line users can run this interface by typing invokeai-configure
### Image Symmetry Options
There are now features to generate horizontal and vertical symmetry during generation. The way these work is to wait until a selected step in the generation process and then to turn on a mirror image effect. In addition to generating some cool images, you can also use this to make side-by-side comparisons of how an image will look with more or fewer steps. Access this option from the WebUI by selecting Symmetry from the image generation settings, or within the CLI by using the options --h_symmetry_time_pct and --v_symmetry_time_pct (these can be abbreviated to --h_sym and --v_sym like all other options).
### A New Unified Canvas Look
This release introduces a beta version of the WebUI Unified Canvas. To try it out, open up the settings dialogue in the WebUI (gear icon) and select Use Canvas Beta Layout:
Refresh the screen and go to to Unified Canvas (left side of screen, third icon from the top). The new layout is designed to provide more space to work in and to keep the image controls close to the image itself:
Model conversion and merging within the WebUI
The WebUI now has an intuitive interface for model merging, as well as for permanent conversion of models from legacy .ckpt/.safetensors formats into diffusers format. These options are also available directly from the invoke.sh/invoke.bat scripts.
An easier way to contribute translations to the WebUI
We have migrated our translation efforts to Weblate, a FOSS translation product. Maintaining the growing project's translations is now far simpler for the maintainers and community. Please review our brief translation guide for more information on how to contribute.
Numerous internal bugfixes and performance issues
### Bug Fixes
This releases quashes multiple bugs that were reported in 2.3.0. Major internal changes include upgrading to diffusers 0.13.0, and using the compel library for prompt parsing. See Detailed Change Log for a detailed list of bugs caught and squished.
Summary of InvokeAI command line scripts (all accessible via the launcher menu)
Command Description
invokeai Command line interface
invokeai --web Web interface
invokeai-model-install Model installer with console forms-based front end
invokeai-ti --gui Textual inversion, with a console forms-based front end
invokeai-merge --gui Model merging, with a console forms-based front end
invokeai-configure Startup configuration; can also be used to reinstall support models
invokeai-update InvokeAI software updater
### Known Bugs in 2.3.1
These are known bugs in the release.
MacOS users generating 768x768 pixel images or greater using diffusers models may experience a hard crash with assertion NDArray > 2**32 This appears to be an issu...
## v2.3.0 <small>(15 January 2023)</small>
**Transition to diffusers
@ -258,13 +488,13 @@ sections describe what's new for InvokeAI.
- A choice of installer scripts that automate installation and configuration.
See
[Installation](installation/index.md).
[Installation](installation/INSTALLATION.md).
- A streamlined manual installation process that works for both Conda and
The app is published in twice, in different build formats.
- A [PyPI] distribution. This includes both a source distribution and built distribution (a wheel). Users install with `pip install invokeai`. The updater uses this build.
- An installer on the [InvokeAI Releases Page]. This is a zip file with install scripts and a wheel. This is only used for new installs.
## General Prep
Make a developer call-out for PRs to merge. Merge and test things out.
While the release workflow does not include end-to-end tests, it does pause before publishing so you can download and test the final build.
## Release Workflow
The `release.yml` workflow runs a number of jobs to handle code checks, tests, build and publish on PyPI.
It is triggered on **tag push**, when the tag matches `v*`. It doesn't matter if you've prepped a release branch like `release/v3.5.0` or are releasing from `main` - it works the same.
> Because commits are reference-counted, it is safe to create a release branch, tag it, let the workflow run, then delete the branch. So long as the tag exists, that commit will exist.
### Triggering the Workflow
Run `make tag-release` to tag the current commit and kick off the workflow.
The release may also be dispatched [manually].
### Workflow Jobs and Process
The workflow consists of a number of concurrently-run jobs, and two final publish jobs.
The publish jobs require manual approval and are only run if the other jobs succeed.
#### `check-version` Job
This job checks that the git ref matches the app version. It matches the ref against the `__version__` variable in `invokeai/version/invokeai_version.py`.
When the workflow is triggered by tag push, the ref is the tag. If the workflow is run manually, the ref is the target selected from the **Use workflow from** dropdown.
This job uses [samuelcolvin/check-python-version].
> Any valid [version specifier] works, so long as the tag matches the version. The release workflow works exactly the same for `RC`, `post`, `dev`, etc.
#### Check and Test Jobs
- **`python-tests`**: runs `pytest` on matrix of platforms
- **`python-checks`**: runs `ruff` (format and lint)
- **`frontend-tests`**: runs `vitest`
- **`frontend-checks`**: runs `prettier` (format), `eslint` (lint), `dpdm` (circular refs), `tsc` (static type check) and `knip` (unused imports)
> **TODO** We should add `mypy` or `pyright` to the **`check-python`** job.
> **TODO** We should add an end-to-end test job that generates an image.
#### `build-installer` Job
This sets up both python and frontend dependencies and builds the python package. Internally, this runs `installer/create_installer.sh` and uploads two artifacts:
- **`dist`**: the python distribution, to be published on PyPI
- **`InvokeAI-installer-${VERSION}.zip`**: the installer to be included in the GitHub release
#### Sanity Check & Smoke Test
At this point, the release workflow pauses as the remaining publish jobs require approval. Time to test the installer.
Because the installer pulls from PyPI, and we haven't published to PyPI yet, you will need to install from the wheel:
- Download and unzip `dist.zip` and the installer from the **Summary** tab of the workflow
- Run the installer script using the `--wheel` CLI arg, pointing at the wheel:
- Install to a temporary directory so you get the new user experience
- Download a model and generate
> The same wheel file is bundled in the installer and in the `dist` artifact, which is uploaded to PyPI. You should end up with the exactly the same installation as if the installer got the wheel from PyPI.
##### Something isn't right
If testing reveals any issues, no worries. Cancel the workflow, which will cancel the pending publish jobs (you didn't approve them prematurely, right?).
Now you can start from the top:
- Fix the issues and PR the fixes per usual
- Get the PR approved and merged per usual
- Switch to `main` and pull in the fixes
- Run `make tag-release` to move the tag to `HEAD` (which has the fixes) and kick off the release workflow again
- Re-do the sanity check
#### PyPI Publish Jobs
The publish jobs will run if any of the previous jobs fail.
They use [GitHub environments], which are configured as [trusted publishers] on PyPI.
Both jobs require a maintainer to approve them from the workflow's **Summary** tab.
- Click the **Review deployments** button
- Select the environment (either `testpypi` or `pypi`)
- Click **Approve and deploy**
> **If the version already exists on PyPI, the publish jobs will fail.** PyPI only allows a given version to be published once - you cannot change it. If version published on PyPI has a problem, you'll need to "fail forward" by bumping the app version and publishing a followup release.
##### Failing PyPI Publish
Check the [python infrastructure status page] for incidents.
If there are no incidents, contact @hipsterusername or @lstein, who have owner access to GH and PyPI, to see if access has expired or something like that.
#### `publish-testpypi` Job
Publishes the distribution on the [Test PyPI] index, using the `testpypi` GitHub environment.
This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release.
If approved and successful, you could try out the test release like this:
Publishes the distribution on the production PyPI index, using the `pypi` GitHub environment.
## Publish the GitHub Release with installer
Once the release is published to PyPI, it's time to publish the GitHub release.
1. [Draft a new release] on GitHub, choosing the tag that triggered the release.
1. Write the release notes, describing important changes. The **Generate release notes** button automatically inserts the changelog and new contributors, and you can copy/paste the intro from previous releases.
1. Use `scripts/get_external_contributions.py` to get a list of external contributions to shout out in the release notes.
1. Upload the zip file created in **`build`** job into the Assets section of the release notes.
1. Check **Set as a pre-release** if it's a pre-release.
1. Check **Create a discussion for this release**.
1. Publish the release.
1. Announce the release in Discord.
> **TODO** Workflows can create a GitHub release from a template and upload release assets. One popular action to handle this is [ncipollo/release-action]. A future enhancement to the release process could set this up.
## Manual Build
The `build installer` workflow can be dispatched manually. This is useful to test the installer for a given branch or tag.
No checks are run, it just builds.
## Manual Release
The `release` workflow can be dispatched manually. You must dispatch the workflow from the right tag, else it will fail the version check.
This functionality is available as a fallback in case something goes wonky. Typically, releases should be triggered via tag push as described above.
Invoke AI originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
# Methods of Contributing to Invoke AI
Anyone who wishes to contribute to InvokeAI, whether features, bug fixes, code cleanup, testing, code reviews, documentation or translation is very much encouraged to do so.
## Development
If you’d like to help with development, please see our [development guide](contribution_guides/development.md).
**New Contributors:** If you’re unfamiliar with contributing to open source projects, take a look at our [new contributor guide](contribution_guides/newContributorChecklist.md).
## Nodes
If you’d like to add a Node, please see our [nodes contribution guide](../nodes/contributingNodes.md).
## Support and Triaging
Helping support other users in [Discord](https://discord.gg/ZmtBAhwWhy) and on Github are valuable forms of contribution that we greatly appreciate.
We receive many issues and requests for help from users. We're limited in bandwidth relative to our the user base, so providing answers to questions or helping identify causes of issues is very helpful. By doing this, you enable us to spend time on the highest priority work.
## Documentation
If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documentation.md).
## Translation
If you'd like to help with translation, please see our[translation guide](contribution_guides/translation.md).
## Tutorials
Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
We hope you enjoy using our software as much as we enjoy creating it, and we hope that some of those of you who are reading this will elect to become part of our contributor community.
# Contributors
This project is a combined effort of dedicated people from across the world.[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
# Code of Conduct
The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
By making a contribution to this project, you certify that:
1. The contribution was created in whole or in part by you and you have the right to submit it under the open-source license indicated in this project’s GitHub repository; or
2. The contribution is based upon previous work that, to the best of your knowledge, is covered under an appropriate open-source license and you have the right under that license to submit that work with modifications, whether created in whole or in part by you, under the same open-source license (unless you are permitted to submit under a different license); or
3. The contribution was provided directly to you by some other person who certified (1) or (2) and you have not modified it; or
4. You understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information you submit with it, including your sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open-source license(s) involved.
This disclaimer is not a license and does not grant any rights or permissions. You must obtain necessary permissions and licenses, including from third parties, before contributing to this project.
This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.
# Support
For support, please use this repository's [GitHub Issues](https://github.com/invoke-ai/InvokeAI/issues), or join the [Discord](https://discord.gg/ZmtBAhwWhy).
Original portions of the software are Copyright (c) 2023 by respective contributors.
---
Remember, your contributions help make this project great. We're excited to see what you'll bring to our community!
"error": "Traceback (most recent call last):\n File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 182, in _download_next_item\n self._do_download(job)\n File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 206, in _do_download\n raise HTTPError(resp.reason)\nrequests.exceptions.HTTPError: Not Found\n"
| Name | `strength` | This field is referred to as `strength` |
| Type Hint | `float` | This field must be of type `float` |
| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
- Create a new class that derives from a predefined parent class called
`BaseInvocation`.
- Every Invocation must have a `docstring`that describes what this Invocation
does.
- While not strictly required, we suggest every invocation class name ends in
"Invocation", eg "CropImageInvocation".
- Every Invocation must use the `@invocation` decorator to provide its unique
invocation type. You may also provide its title, tags and category using the
decorator.
- Invocations are strictly typed. We make use of the native
[typing](https://docs.python.org/3/library/typing.html) library and the
installed [pydantic](https://pydantic-docs.helpmanual.io/) library for
validation.
Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this
field to be parsed with `None` as a value, which enables linking to previous
invocations. All fields should either provide a default value or allow `None` as
a value, so that they can be overwritten with a linked output from another
invocation.
The special type `ImageField` is also used here. All images are passed as
`ImageField`, which protects them from pydantic validation errors (since images
only ever come from links).
Finally, note that for all linking, the `type` of the linked fields must match.
If the `name` also matches, then the field can be **automatically linked** to a
previous invocation by name and matching.
### Config
```py
# Schema customisation
classConfig(InvocationConfig):
schema_extra={
"ui":{
"tags":["upscaling","image"],
},
}
```
This is an optional configuration for the invocation. It inherits from
pydantic's model `Config` class, and it used primarily to customize the
autogenerated OpenAPI schema.
The UI relies on the OpenAPI schema in two ways:
- An API client & Typescript types are generated from it. This happens at build
time.
- The node editor parses the schema into a template used by the UI to create the
node editor UI. This parsing happens at runtime.
In this example, a `ui` key has been added to the `schema_extra` dict to provide
some tags for the UI, to facilitate filtering nodes.
See the Schema Generation section below for more information.
Now when you open the VSCode integrated terminal, or do anything that needs to
run python, it will automatically be in your InvokeAI virtual environment.
Bonus: When you create a Jupyter notebook, when you run it, you'll be prompted
for the python interpreter to run in. This will default to your `venv` python,
and so you'll have access to the same python environment as the InvokeAI app.
This is _super_ handy.
#### Enabling Type-Checking with Pylance
We use python's typing system in InvokeAI. PR reviews will include checking that types are present and correct. We don't enforce types with `mypy` at this time, but that is on the horizon.
Using a code analysis tool to automatically type check your code (and types) is very important when writing with types. These tools provide immediate feedback in your editor when types are incorrect, and following their suggestions lead to fewer runtime bugs.
Pylance, installed at the beginning of this guide, is the de-facto python LSP (language server protocol). It provides type checking in the editor (among many other features). Once installed, you do need to enable type checking manually:
- Open a python file
- Look along the status bar in VSCode for `{ } Python`
- Click the `{ }`
- Turn type checking on - basic is fine
You'll now see red squiggly lines where type issues are detected. Hover your cursor over the indicated symbols to see what's wrong.
In 99% of cases when the type checker says there is a problem, there really is a problem, and you should take some time to understand and resolve what it is pointing out.
#### Debugging configs with `launch.json`
Debugging configs are managed in a `launch.json` file. Like most VSCode configs,
these can be scoped to a workspace or folder.
Follow the [official guide](https://code.visualstudio.com/docs/python/debugging)
to set up your `launch.json` and try it out.
Now we can create the InvokeAI debugging configs:
```jsonc
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version":"0.2.0",
"configurations":[
{
// Run the InvokeAI backend & serve the pre-built UI
"name":"InvokeAI Web",
"type":"python",
"request":"launch",
"program":"scripts/invokeai-web.py",
"args":[
// Your InvokeAI root dir (where `invokeai.yaml` lives)
"--root",
"/path/to/invokeai_root",
// Access the app from anywhere on your local network
"--host",
"0.0.0.0"
],
"justMyCode":true
},
{
// Run the nodes-based CLI
"name":"InvokeAI CLI",
"type":"python",
"request":"launch",
"program":"scripts/invokeai-cli.py",
"justMyCode":true
},
{
// Run tests
"name":"InvokeAI Test",
"type":"python",
"request":"launch",
"module":"pytest",
"args":["--capture=no"],
"justMyCode":true
},
{
// Run a single test
"name":"InvokeAI Single Test",
"type":"python",
"request":"launch",
"module":"pytest",
"args":[
// Change this to point to the specific test you are working on
"tests/nodes/test_invoker.py"
],
"justMyCode":true
},
{
// This is the default, useful to just run a single file
"name":"Python: File",
"type":"python",
"request":"launch",
"program":"${file}",
"justMyCode":true
}
]
}
```
You'll see these configs in the debugging configs drop down. Running them will
start InvokeAI with attached debugger, in the correct environment, and work just
like the normal app.
Enjoy debugging InvokeAI with ease (not that we have any bugs of course).
#### Remote dev
This is very easy to set up and provides the same very smooth experience as
local development. Environments and debugging, as set up above, just work,
though you'd need to recreate the workspace and debugging configs on the remote.
Consult the
[official guide](https://code.visualstudio.com/docs/remote/remote-overview) to
get it set up.
Suggest using VSCode's included settings sync so that your remote dev host has
all the same app settings and extensions automagically.
##### One remote dev gotcha
I've found the automatic port forwarding to be very flakey. You can disable it
in `Preferences: Open Remote Settings (ssh: hostname)`. Search for
`remote.autoForwardPorts` and untick the box.
To forward ports very reliably, use SSH on the remote dev client (e.g. your
macbook). Here's how to forward both backend API port (`9090`) and the frontend
live dev server port (`5173`):
```bash
ssh \
-L 9090:localhost:9090 \
-L 5173:localhost:5173 \
user@remote-dev-host
```
The forwarding stops when you close the terminal window, so suggest to do this
_outside_ the VSCode integrated terminal in case you need to restart VSCode for
an extension update or something
Now, on your remote dev client, you can open `localhost:9090` and access the UI,
now served from the remote dev host, just the same as if it was running on the
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