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
