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>
