[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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## 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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software specifications as well as any other pertinent information.
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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 #
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## 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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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
- [ ] 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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pull
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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.
