* 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.
