## 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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- Depends on #5007
## QA Instructions, Screenshots, Recordings
N/A
<!--
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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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merged"
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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.
